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ISPRS Int. J. Geo-Inf., Volume 9, Issue 6 (June 2020) – 72 articles

Cover Story (view full-size image): In the last two decades, unmanned aircraft systems (UAS) have successfully been used in different environments for diverse applications. For UAS LiDAR-based mapping missions, the requirements for flight planning differ from those of conventional UAS image-based flight plans because of various reasons related to the LiDAR scanning mechanism, scanning range, output scanning rate, field of view (FOV), and rotation speed, amongst others. The article presents flight planning simulations in which the UAS platform is equipped or, alternatively, three low-cost multi-beam LiDARs, namely Quanergy M8, Velodyne VLP-16, and Ouster OS-1-16. The specific characteristics of the three sensors were used to plan flights and acquire dense point clouds. The results show clear relationships between point density, flying speeds, and flying heights. View this paper
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17 pages, 7439 KiB  
Article
An Evaluation Model of Level of Detail Consistency of Geographical Features on Digital Maps
by Pengcheng Liu and Jia Xiao
ISPRS Int. J. Geo-Inf. 2020, 9(6), 410; https://doi.org/10.3390/ijgi9060410 - 26 Jun 2020
Cited by 2 | Viewed by 2284
Abstract
This paper proposes a method to evaluate the level of detail (LoD) of geographic features on digital maps and assess their LoD consistency. First, the contour of the geometry of the geographic feature is sketched and the hierarchy of its graphical units is [...] Read more.
This paper proposes a method to evaluate the level of detail (LoD) of geographic features on digital maps and assess their LoD consistency. First, the contour of the geometry of the geographic feature is sketched and the hierarchy of its graphical units is constructed. Using the quartile measurement method of statistical analysis, outliers of graphical units are eliminated and the average value of the graphical units below the bottom quartile is used as the statistical LoD parameter for a given data sample. By comparing the LoDs of homogeneous and heterogeneous features, we analyze the differences between the nominal scale and actual scale to evaluate the LoD consistency of features on a digital map. The validation of this method is demonstrated by experiments conducted on contour lines at a 1:5K scale and artificial building polygon data at scales of 1:2K and 1:5K. The results show that our proposed method can extract the scale of features on maps and evaluate their LoD consistency. Full article
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<p>Detecting the LoD of a curve using the progressive increment method. (<b>a</b>) A curve with bends. (<b>b</b>–<b>d</b>) Curve representation when the symbol width is respectively 1m, 2m and 3.5m. (<b>e</b>–<b>g</b>) Rasterizing results of curves, respectively, at 1m, 2m and 3.5m.</p> Full article ">Figure 2
<p>The process for identifying the graphical units of a curve. (<b>a</b>) An example curve with the order number of points on the curve. (<b>b</b>) Delaunay triangulation constructed with the curve points. (<b>c</b>) The triangulation sets on the left side of the forward direction of the curve. (<b>d</b>) The triangulation sets on the right of the forward direction of the curve. (<b>e</b>) The left-bend sets on the left of the forward direction of the curve. (<b>f</b>) The right-bend sets on the left of the forward direction of the curve. (<b>g</b>) The hierarchical structure of the left-side curve. (<b>h</b>) The hierarchical structure of the right-side curve.</p> Full article ">Figure 3
<p>Judging the position of Point <span class="html-italic">P</span>, <span class="html-italic">Q</span> relative to the curve <span class="html-italic">L</span>.</p> Full article ">Figure 4
<p>The schematic map of the width (w) and depth (d) of a bend.</p> Full article ">Figure 5
<p>Schematic map of the size and direction of the intersection angle.</p> Full article ">Figure 6
<p>The (<b>A</b>) and (<b>B</b>) leaf bends and four close-ups along the No. 6 contour line in <a href="#ijgi-09-00410-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 7
<p>Boxplots of the LoD parameters based on the left bends, right bends, and all bends for six contour lines (the “+” symbol represents abnormal values). (<b>a</b>) Boxplot of the LoD parameters for the No.1 contour line. (<b>b</b>) Boxplot of the LoD parameters for the No.1 contour line. (<b>c</b>) Boxplot of the LoD parameters for the No.3 contour line. (<b>d</b>) Boxplot of the LoD parameters for the No.4 contour line. (<b>e</b>) Boxplot of the LoD parameters for the No.5 contour line. (<b>f</b>) Boxplot of the LoD parameters for the No.6 contour line.</p> Full article ">Figure 8
<p>Boxplots of the LoD parameters based on the leaf bends of the six curves (after removing the outliers).</p> Full article ">Figure 9
<p>Building polygon experimental data and detected graphical units. (<b>a</b>) 1:2K building polygon data. (<b>b</b>) Graphical units within the red box in <a href="#ijgi-09-00410-f009" class="html-fig">Figure 9</a>a. (<b>c</b>) 1:5K scale building polygon data. (<b>d</b>) Close-up of graphical units within the red box in <a href="#ijgi-09-00410-f009" class="html-fig">Figure 9</a>c.</p> Full article ">Figure 10
<p>Boxplot of LoDs for the building polygons from 1:2K (<b>a</b>) and 1:5K (<b>b</b>) maps.</p> Full article ">Figure 11
<p>Replacing some large-scale geographic features into a small-scale map. (<b>a</b>) Original small-scale map. (<b>b</b>) Mixed map. (The red polylines are the original small-scale geographic features with a scale of 1:2,000,000; the blue polylines represent “Wrong Data” on a large scale, with a scale of 1:750,000).</p> Full article ">Figure 12
<p>Replacing some large-scale buildings into a small-scale map. (<b>a</b>) Original small-scale buildings. (<b>b</b>) Large scale buildings for replacement. (<b>c</b>) Mixed map 1. (<b>d</b>) Mixed map 2. (The red polygons represent the original small-scale geographic buildings with a scale of 1:30,000, the blue polygons represent “Wrong Data” on a large scale, with a scale of 1:10,000).</p> Full article ">
24 pages, 9318 KiB  
Article
Accounting for Local Geological Variability in Sequential Simulations—Concept and Application
by Adrian Linsel, Sebastian Wiesler, Joshua Haas, Kristian Bär and Matthias Hinderer
ISPRS Int. J. Geo-Inf. 2020, 9(6), 409; https://doi.org/10.3390/ijgi9060409 - 26 Jun 2020
Cited by 6 | Viewed by 3364
Abstract
Heterogeneity-preserving property models of subsurface regions are commonly constructed by means of sequential simulations. Sequential Gaussian simulation (SGS) and direct sequential simulation (DSS) draw values from a local probability density function that is described by the simple kriging estimate and the local simple [...] Read more.
Heterogeneity-preserving property models of subsurface regions are commonly constructed by means of sequential simulations. Sequential Gaussian simulation (SGS) and direct sequential simulation (DSS) draw values from a local probability density function that is described by the simple kriging estimate and the local simple kriging variance at unsampled locations. The local simple kriging variance, however, does not necessarily reflect the geological variability being present at subsets of the target domain. In order to address that issue, we propose a new workflow that implements two modified versions of the popular SGS and DSS algorithms. Both modifications, namely, LVM-DSS and LVM-SGS, aim at simulating values by means of introducing a local variance model (LVM). The LVM is a measurement-constrained and geology-driven global representation of the locally observable variance of a property. The proposed modified algorithms construct the local probability density function with the LVM instead of using the simple kriging variance, while still using the simple kriging estimate as the best linear unbiased estimator. In an outcrop analog study, we can demonstrate that the local simple kriging variance in sequential simulations tends to underestimate the locally observed geological variability in the target domain and certainly does not account for the spatial distribution of the geological heterogeneity. The proposed simulation algorithms reproduce the global histogram, the global heterogeneity, and the considered variogram model in the range of ergodic fluctuations. LVM-SGS outperforms the other algorithms regarding the reproduction of the variogram model. While DSS and SGS generate a randomly distributed heterogeneity, the modified algorithms reproduce a geologically reasonable spatial distribution of heterogeneity instead. The new workflow allows for the integration of continuous geological trends into sequential simulations rather than using class-based approaches such as the indicator simulation technique. Full article
(This article belongs to the Special Issue Uncertainty Modeling in Spatial Data Analysis)
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<p>Conceptualization of a regionalized variable after [<a href="#B5-ijgi-09-00409" class="html-bibr">5</a>] exemplary illustrated for the intrinsic permeability.</p> Full article ">Figure 2
<p>Schematic of the uncertainty components integrated into a predictive model of rock properties. (<b>a</b>) Illustration of an interpolation process using neighboring points <math display="inline"><semantics> <msub> <mi>x</mi> <mi>k</mi> </msub> </semantics></math> with known values to predict the unknown value at <math display="inline"><semantics> <msub> <mi>x</mi> <mn>0</mn> </msub> </semantics></math>. (<b>b</b>–<b>d</b>) Schematic of the local probability density functions (PDFs) in form of a Gaussian distribution defined by <math display="inline"><semantics> <msup> <mi>σ</mi> <mn>2</mn> </msup> </semantics></math> and <math display="inline"><semantics> <mi>μ</mi> </semantics></math> for the estimated kriging error variance <math display="inline"><semantics> <msubsup> <mi>σ</mi> <mrow> <mi>S</mi> <mi>K</mi> </mrow> <mn>2</mn> </msubsup> </semantics></math> at <math display="inline"><semantics> <msub> <mi>x</mi> <mn>0</mn> </msub> </semantics></math> (<b>b</b>), the observed measurement error <math display="inline"><semantics> <msubsup> <mi>σ</mi> <mi>m</mi> <mn>2</mn> </msubsup> </semantics></math> at the point <math display="inline"><semantics> <msub> <mi>x</mi> <mn>3</mn> </msub> </semantics></math> (<b>c</b>) and the observed variance <math display="inline"><semantics> <msubsup> <mi>σ</mi> <mi>b</mi> <mn>2</mn> </msubsup> </semantics></math> in a subset <math display="inline"><semantics> <msub> <mo>Ω</mo> <mi>b</mi> </msub> </semantics></math> of <math display="inline"><semantics> <mo>Ω</mo> </semantics></math> (<b>d</b>).</p> Full article ">Figure 3
<p>(<b>a</b>) Photogrammetric model of the investigated sandstone quarry. The outcrop is compartmentalized by two scissor faults and consists of two lacustrine-deltaic Bouma sequences [<a href="#B40-ijgi-09-00409" class="html-bibr">40</a>]. (<b>b</b>) Sedimentological 1-D section of the sedimentary architecture observed in the outcrop. The Bouma sequence provides an erosive base. One sequence is characterized by a fining-upward trend and consists of intraclasts-rich massive sandstones at the base and trough cross-bedded and ripple cross-bedded sandstones towards top [<a href="#B40-ijgi-09-00409" class="html-bibr">40</a>]. (<b>c</b>) Spatial interpolation of a PDF exemplary illustrated with both theoretical Gaussian distributions derived from the measurements of OSB1_c and OSB2_c.</p> Full article ">Figure 4
<p>(<b>a</b>) Photogrammetric model of the investigated sandstone quarry in Obersulzbach, Germany. Sample locations are displayed as spheres, whose color indicates the observed permeability value at the sample locations. (<b>b</b>) Hexahedral non-orthogonal mesh of the investigated outcrop generated by an IDW interpolation using the nodes of the photogrammetric model as constraints.</p> Full article ">Figure 5
<p>Empirical variogram and variogram model, empirical histogram, and heterogeneity-indexes derived from the <span class="html-italic">k</span> measurements for the outcrop (<b>a</b>–<b>c</b>), and the rock cubes OSB1_c (<b>d</b>–<b>f</b>) and OSB2_c (<b>g</b>–<b>i</b>). A scale-effect is observable in the heterogeneity-indicating coefficient of variation, the Dykstra–Parson coefficient and the sample variance. All variogram models are described by a spherical model with nugget effect. The variogram model for (<b>a</b>) is described by n = 0.05 mD<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>, a = 23 m and b = 0.75 mD<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math> with n as nugget, a as range, and b as sill. The model for (<b>d</b>) is described by n = 0 mD<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>, a = 0.3 m and b = 0.58 mD<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math> while the model of (<b>g</b>) is described by n = 0.005 mD<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>, a = 0.18 m and b = 0.08 mD<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>.</p> Full article ">Figure 6
<p>Spatial distribution of the intrinsic permeability in the rock cubes OSB1_c (<b>a</b>) [<a href="#B40-ijgi-09-00409" class="html-bibr">40</a>] and OSB2_c (<b>b</b>) interpolated using the SK method.</p> Full article ">Figure 7
<p>(<b>a</b>) Mapping of the local variance with regard to the observed geological structure. The highest variance is indicated by red spheres whereas the lowest variance is indicated by blue ones. The variance is derived from the rock cube measurements of OSB1_c—representing the most heterogeneous lithology at the bottom of the Bouma sequences (red)—and OSB2_c—likewise representing the most homogeneous lithology at the top of the Bouma sequences (blue). (<b>b</b>) The 3-D local variance model (LVM) representing the locally observable variance, which is constrained by the mappings shown in (<b>a</b>).</p> Full article ">Figure 8
<p>Results of the linear integer programming optimization using the marked sampling points. The interpolation error <math display="inline"><semantics> <msub> <mi>ϵ</mi> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> </msub> </semantics></math> is minimized using the inequality constraints given in Equation (22). (<b>a</b>) RMSE response surface with regard to the incorporated measurement error variance <math display="inline"><semantics> <msubsup> <mi>σ</mi> <mi>m</mi> <mn>2</mn> </msubsup> </semantics></math> and the maximum number of neighbors <math display="inline"><semantics> <msub> <mi>n</mi> <mi>n</mi> </msub> </semantics></math> using a leave-one-out cross-validation. (<b>b</b>) Cross sections through the response surface of (<b>a</b>).</p> Full article ">Figure 9
<p>(<b>a</b>) Simple kriging estimate (<b>b</b>) and the local simple kriging variance for one SK realization.</p> Full article ">Figure 10
<p>(<b>a</b>) Comparison of the empirical histograms of the <math display="inline"><semantics> <msubsup> <mi>σ</mi> <mrow> <mi>S</mi> <mi>K</mi> </mrow> <mn>2</mn> </msubsup> </semantics></math> model produced in a DSS realization with the LVM and (<b>b</b>) the empirical distribution of <math display="inline"><semantics> <msubsup> <mi>σ</mi> <mrow> <mi>S</mi> <mi>K</mi> </mrow> <mn>2</mn> </msubsup> </semantics></math> produced in the realization of (<b>a</b>).</p> Full article ">Figure 11
<p>Experimental variograms (gray) for 15 realizations of DSS (<b>a</b>), SGS (<b>b</b>), LVM-DSS (<b>c</b>) and LVM-SGS (<b>d</b>) plotted together with the average over all realizations (blue) and the considered variogram model (red), which is described by a nugget of 0.05 mD<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>, a range of 23 m and a sill of 0.75 mD<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>.</p> Full article ">Figure 12
<p>Exemplary model visualizations for the DSS, SGS, LVM-DSS and LVM-SGS realizations.</p> Full article ">Figure 13
<p>Top-view onto a representative simulation result of DSS (<b>a</b>) and LVM-DSS (<b>b</b>) superimposed by a gray-scale representation of the LVM with an opacity of 0.6. It is evident that the LVM-based algorithms’ heterogeneity is highest in that area of the LVM in which it provides the highest local variance as well. The conventional approach, however, does not reflect the expected variance in space. (<b>c</b>) Conceptual illustration showing the spatial distribution of the constraining measurements <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>(</mo> <msub> <mi>x</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> </semantics></math> and the spatial relationship between the simple kriging estimate <math display="inline"><semantics> <msub> <mi>μ</mi> <mrow> <mi>S</mi> <mi>K</mi> </mrow> </msub> </semantics></math> with the measurement error <math display="inline"><semantics> <msub> <mi>ϵ</mi> <mi>m</mi> </msub> </semantics></math> and the two parameters used to simulate <span class="html-italic">k</span> in this study namely <math display="inline"><semantics> <msubsup> <mi>σ</mi> <mrow> <mi>S</mi> <mi>K</mi> </mrow> <mn>2</mn> </msubsup> </semantics></math> and <math display="inline"><semantics> <msubsup> <mi>σ</mi> <mrow> <mi>L</mi> <mi>V</mi> <mi>M</mi> </mrow> <mn>2</mn> </msubsup> </semantics></math>. Pr stands for the probability of <span class="html-italic">k</span> under the condition that <span class="html-italic">k</span> belongs to the Gaussian distribution described by <math display="inline"><semantics> <msub> <mi>μ</mi> <mrow> <mi>S</mi> <mi>K</mi> </mrow> </msub> </semantics></math> together with either <math display="inline"><semantics> <msubsup> <mi>σ</mi> <mrow> <mi>S</mi> <mi>K</mi> </mrow> <mn>2</mn> </msubsup> </semantics></math> or <math display="inline"><semantics> <msubsup> <mi>σ</mi> <mrow> <mi>L</mi> <mi>V</mi> <mi>M</mi> </mrow> <mn>2</mn> </msubsup> </semantics></math>.</p> Full article ">
23 pages, 4408 KiB  
Article
A Symbiotic Relationship Based Leader Approach for Privacy Protection in Location Based Services
by Hosam Alrahhal, Mohamad Shady Alrahhal, Razan Jamous and Kamal Jambi
ISPRS Int. J. Geo-Inf. 2020, 9(6), 408; https://doi.org/10.3390/ijgi9060408 - 26 Jun 2020
Cited by 11 | Viewed by 2585
Abstract
Location-based services (LBS) form the main part of the Internet of Things (IoT) and have received a significant amount of attention from the research community as well as application users due to the popularity of wireless devices and the daily growth in users. [...] Read more.
Location-based services (LBS) form the main part of the Internet of Things (IoT) and have received a significant amount of attention from the research community as well as application users due to the popularity of wireless devices and the daily growth in users. However, there are several risks associated with the use of LBS-enabled applications, as users are forced to send their queries based on their real-time and actual location. Attacks could be applied by the LBS server itself or by its maintainer, which consequently may lead to more serious issues such as the theft of sensitive and personal information about LBS users. Due to this fact, complete privacy protection (location and query privacy protection) is a critical problem. Collaborative (cache-based) approaches are used to prevent the LBS application users from connecting to the LBS server (malicious parties). However, no robust trust approaches have been provided to design a trusted third party (TTP), which prevents LBS users from acting as an attacker. This paper proposed a symbiotic relationship-based leader approach to guarantee complete privacy protection for users of LBS-enabled applications. Specifically, it introduced the mutual benefit underlying the symbiotic relationship, dummies, and caching concepts to avoid dealing with untrusted LBS servers and achieve complete privacy protection. In addition, the paper proposed a new privacy metric to predict the closeness of the attacker to the moment of her actual attack launch. Compared to three well-known approaches, namely enhanced dummy location selection (enhanced-DLS), hiding in a mobile crowd, and caching-aware dummy selection algorithm (enhanced-CaDSA), our experimental results showed better performance in terms of communication cost, resistance against inferences attacks, and cache hit ratio. Full article
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<p>The classical scenario of LBS applications.</p> Full article ">Figure 2
<p>Classification of LBS privacy protection approaches.</p> Full article ">Figure 3
<p>General scenario of cache-based approach.</p> Full article ">Figure 4
<p>Proposed system model scenario.</p> Full article ">Figure 5
<p>Local reputations of cluster members.</p> Full article ">Figure 6
<p>Resetting global reputation of moving Leader.</p> Full article ">Figure 7
<p>Homogeneity attack [<a href="#B42-ijgi-09-00408" class="html-bibr">42</a>].</p> Full article ">Figure 8
<p>Query sampling attack [<a href="#B15-ijgi-09-00408" class="html-bibr">15</a>].</p> Full article ">Figure 9
<p>The profile of the LBS user specialized on the attacker side.</p> Full article ">Figure 10
<p>Our proposed privacy metric.</p> Full article ">Figure 11
<p>Communication cost VS. Time progress.</p> Full article ">Figure 12
<p>Communication cost VS. Anonymity level.</p> Full article ">Figure 13
<p><math display="inline"><semantics> <mi>λ</mi> </semantics></math> values for 20 Leaders, <math display="inline"><semantics> <mrow> <mi mathvariant="normal">k</mi> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math>.</p> Full article ">Figure 14
<p>Cache hit ratio vs. time progress, p=100.</p> Full article ">
18 pages, 6859 KiB  
Article
A Simplified Method of Cartographic Visualisation of Buildings’ Interiors (2D+) for Navigation Applications
by Dariusz Gotlib, Michał Wyszomirski and Miłosz Gnat
ISPRS Int. J. Geo-Inf. 2020, 9(6), 407; https://doi.org/10.3390/ijgi9060407 - 26 Jun 2020
Cited by 8 | Viewed by 3424
Abstract
This article proposes an original method of a coherent and simplified cartographic presentation of the interior of buildings called 2D+, which can be used in geoinformation applications that do not support an extensive three-dimensional visualisation or do not have access to a 3D [...] Read more.
This article proposes an original method of a coherent and simplified cartographic presentation of the interior of buildings called 2D+, which can be used in geoinformation applications that do not support an extensive three-dimensional visualisation or do not have access to a 3D model of the building. A simplified way of cartographic visualisation can be used primarily in indoor navigation systems and other location-based services (LBS) applications. It can also be useful in systems supporting facility management (FM) and various kinds of geographic information systems (GIS). On the one hand, it may increase an application’s efficiency; on the other, it may unify the method of visualisation in the absence of a building’s 3D model. Thanks to the proposed method, it is possible to achieve the same effect regardless of the data source used: Building Information Modelling (BIM), a Computer-aided Design (CAD) model, or traditional architectural and construction drawings. Such a solution may be part of a broader concept of a multi-scale presentation of buildings’ interiors. The article discusses the issues of visualising data and converting data to the appropriate coordinate system, as well as the properties of the application model of data. Full article
(This article belongs to the Special Issue Geovisualization and Map Design)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>A simplified way of showing the third dimension on maps supporting navigation in buildings (<b>a</b>) Google Maps application (source: Engadget); (<b>b</b>) The map of the Wrocław airport presented on information kiosks [<a href="#B10-ijgi-09-00407" class="html-bibr">10</a>]</p> Full article ">Figure 2
<p>Interactive panorama of the building’s interior. 2D cartographic presentation is visible on the right. A floor number switch is visible on the top bar.</p> Full article ">Figure 3
<p>An axonometric view of a building used, among others, at information kiosks in shopping centres [<a href="#B29-ijgi-09-00407" class="html-bibr">29</a>].</p> Full article ">Figure 4
<p>A diagram of preparing the drawing documentation for the proposed navigation application and inputting the data into the database that the application uses.</p> Full article ">Figure 5
<p>A diagram of the 2D+ system’s concept of operation: projecting the route of a navigation application’s user, both of the floor plan and the vertical section of the building.</p> Full article ">Figure 6
<p>An illustration of the idea of switching the display between a floor plan and a vertical section of a building in navigation applications.</p> Full article ">Figure 7
<p>Linking the cross-sectional line, pictured in the horizontal projection, and the entire cross-section recorded in the database. The colours of the arrows are consistent with the colours of the expected cross-sections.</p> Full article ">Figure 8
<p>The solution in which the floor plan <b>[f1]</b> and vertical cross-sections <b>[s1]</b> and <b>[s2]</b> are all embedded in their local 2D coordinate systems. The building’s model <b>[B]</b> is recorded in the 3D coordinate system. Between separate local systems and the building’s reference system are transformation matrices <b>M<sub>f1</sub></b>, <b>M<sub>s1</sub></b>, <b>M<sub>s2</sub></b>.</p> Full article ">Figure 9
<p>The solution in which the floor plan <b>[f1]</b> and the cross-sections <b>[s1]</b> and <b>[s2]</b> are embedded in the same coordinate system as the building’s model <b>[B]</b>.</p> Full article ">Figure 10
<p>The concept of route visualisation: (<b>a</b>) an outlook presentation of the route on the whole building in 3D; (<b>b</b>) visualisation of the route on the floor plan in 2D; (<b>c</b>) and (<b>d</b>) visualisation of the route on the cross-sections; (<b>e</b>) the legend for the conceptual visualisation of the route in the cross-section.</p> Full article ">Figure 10 Cont.
<p>The concept of route visualisation: (<b>a</b>) an outlook presentation of the route on the whole building in 3D; (<b>b</b>) visualisation of the route on the floor plan in 2D; (<b>c</b>) and (<b>d</b>) visualisation of the route on the cross-sections; (<b>e</b>) the legend for the conceptual visualisation of the route in the cross-section.</p> Full article ">Figure 11
<p>The illustration of selecting the correct section, including the user’s position and the direction of their gaze.</p> Full article ">Figure 12
<p>The method for selecting vertical cross-sections of the building in the mobile application for the user located: (<b>a</b>) near the centre of a rectangular room; (<b>b</b>) near one of the walls of the rectangular room; (<b>c</b>) near the centre of an elongated room, e.g., a corridor.</p> Full article ">Figure 13
<p>A general diagram of the algorithm for the 2D+ navigation application.</p> Full article ">
20 pages, 14033 KiB  
Article
Experiment in Finding Look-Alike European Cities Using Urban Atlas Data
by Zdena Dobesova
ISPRS Int. J. Geo-Inf. 2020, 9(6), 406; https://doi.org/10.3390/ijgi9060406 - 26 Jun 2020
Cited by 9 | Viewed by 3500
Abstract
The integration of geography and machine learning can produce novel approaches in addressing a variety of problems occurring in natural and human environments. This article presents an experiment that identifies cities that are similar according to their land use data. The article presents [...] Read more.
The integration of geography and machine learning can produce novel approaches in addressing a variety of problems occurring in natural and human environments. This article presents an experiment that identifies cities that are similar according to their land use data. The article presents interesting preliminary experiments with screenshots of maps from the Czech map portal. After successfully working with the map samples, the study focuses on identifying cities with similar land use structures. The Copernicus European Urban Atlas 2012 was used as a source dataset (data valid years 2015–2018). The Urban Atlas freely offers land use datasets of nearly 800 functional urban areas in Europe. To search for similar cities, a set of maps detailing land use in European cities was prepared in ArcGIS. A vector of image descriptors for each map was subsequently produced using a pre-trained neural network, known as Painters, in Orange software. As a typical data mining task, the nearest neighbor function analyzes these descriptors according to land use patterns to find look-alike cities. Example city pairs based on land use are also presented in this article. The research question is whether the existing pre-trained neural network outside cartography is applicable for categorization of some thematic maps with data mining tasks such as clustering, similarity, and finding the nearest neighbor. The article’s contribution is a presentation of one possible method to find cities similar to each other according to their land use patterns, structures, and shapes. Some of the findings were surprising, and without machine learning, could not have been evident through human visual investigation alone. Full article
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Workflow in Orange with widgets from the Image Analytics add-on.</p> Full article ">Figure 2
<p>Table of enhanced output data with image descriptors (attributes <span class="html-italic">n0</span>, <span class="html-italic">n1</span>, <span class="html-italic">n2</span> …) for each source image processed using the Image Analytics widget (only partial extract of descriptors).</p> Full article ">Figure 3
<p>Example data: (<b>a</b>) categories of land use in Urban Atlas 2012; (<b>b</b>) circular extract of the city of Salzburg, source: [<a href="#B29-ijgi-09-00406" class="html-bibr">29</a>], final validation 29 June 2018; circular extract – author’s work.</p> Full article ">Figure 4
<p>Workflow with the Image Embedding and Neighbors widgets in Orange.</p> Full article ">Figure 5
<p>Two pairs with similar maps: (<b>a</b>) neighbor maps with water bodies; (<b>b</b>) neighbor maps with first and second class roads (extracts are author’s work from portal: <a href="https://mapy.cz" target="_blank">https://mapy.cz</a>, accessed 10 September 2019).</p> Full article ">Figure 6
<p>Image grid in Orange with the thumbnail of the tested map, with four evident clusters arranged according to base maps, historical, aerial, and black and white (author’s work).</p> Full article ">Figure 7
<p>Map category prediction: (<b>a</b>) workflow for predicting map category using the Logistic Regression and Prediction widgets; (<b>b</b>) output table of predictions with probabilities for four maps.</p> Full article ">Figure 7 Cont.
<p>Map category prediction: (<b>a</b>) workflow for predicting map category using the Logistic Regression and Prediction widgets; (<b>b</b>) output table of predictions with probabilities for four maps.</p> Full article ">Figure 8
<p>Interesting look-alike city pairs. (Source: [<a href="#B29-ijgi-09-00406" class="html-bibr">29</a>], final validation Zalaegerszeg 29 June 2018, Žilina 16 December 2015, Novi Sad 22 May 2018, Tarbes 29 January 2016; circular extracts – author’s work).</p> Full article ">Figure 9
<p>An interesting triplet of look-alike cities. (<b>a</b>) Odense; (<b>b</b>) Metz; (<b>c</b>) Münster. (Source: [<a href="#B29-ijgi-09-00406" class="html-bibr">29</a>], Metz 16 December 2015, Münster 23 March 2018; circular extracts – author’s work).</p> Full article ">Figure 9 Cont.
<p>An interesting triplet of look-alike cities. (<b>a</b>) Odense; (<b>b</b>) Metz; (<b>c</b>) Münster. (Source: [<a href="#B29-ijgi-09-00406" class="html-bibr">29</a>], Metz 16 December 2015, Münster 23 March 2018; circular extracts – author’s work).</p> Full article ">Figure A1
<p>Pairs of similar cities according to land use produced by hierarchical clustering. (<b>a</b>) Modena (Italy); (<b>b</b>) Parma (Italy); (<b>c</b>) Perpignan (France); (<b>d</b>) Plovdiv (Bulgaria); (<b>e</b>) Basel (Switzerland); (<b>f</b>) Bielsko-Biala (Poland); (<b>g</b>) Perugia (Italy); (<b>h</b>) Plauen (Germany); (<b>i</b>) Orléans (France); (<b>j</b>) Poznaň (Poland); (<b>k</b>) Guimares (Portugal); (<b>l</b>) Osnabrück (Germany); (<b>m</b>) Augsburg (Germany); (<b>n</b>) Aviles (Spain); (<b>o</b>) Ljubljana (Slovenia); (<b>p</b>) Lübeck (Germany); (<b>q</b>) Enschede (Netherlands); (<b>r</b>) Oviedo (Spain); (<b>s</b>) Glogow (Poland); (<b>t</b>) Maastricht (Netherlands); (<b>u</b>) České Budějovice (Czech Republic); (<b>v</b>) Hradec Králové (Czech Republic); (<b>w</b>) Crawley (UK); (<b>x</b>) Örebro (Sweden).</p> Full article ">Figure A1 Cont.
<p>Pairs of similar cities according to land use produced by hierarchical clustering. (<b>a</b>) Modena (Italy); (<b>b</b>) Parma (Italy); (<b>c</b>) Perpignan (France); (<b>d</b>) Plovdiv (Bulgaria); (<b>e</b>) Basel (Switzerland); (<b>f</b>) Bielsko-Biala (Poland); (<b>g</b>) Perugia (Italy); (<b>h</b>) Plauen (Germany); (<b>i</b>) Orléans (France); (<b>j</b>) Poznaň (Poland); (<b>k</b>) Guimares (Portugal); (<b>l</b>) Osnabrück (Germany); (<b>m</b>) Augsburg (Germany); (<b>n</b>) Aviles (Spain); (<b>o</b>) Ljubljana (Slovenia); (<b>p</b>) Lübeck (Germany); (<b>q</b>) Enschede (Netherlands); (<b>r</b>) Oviedo (Spain); (<b>s</b>) Glogow (Poland); (<b>t</b>) Maastricht (Netherlands); (<b>u</b>) České Budějovice (Czech Republic); (<b>v</b>) Hradec Králové (Czech Republic); (<b>w</b>) Crawley (UK); (<b>x</b>) Örebro (Sweden).</p> Full article ">Figure A1 Cont.
<p>Pairs of similar cities according to land use produced by hierarchical clustering. (<b>a</b>) Modena (Italy); (<b>b</b>) Parma (Italy); (<b>c</b>) Perpignan (France); (<b>d</b>) Plovdiv (Bulgaria); (<b>e</b>) Basel (Switzerland); (<b>f</b>) Bielsko-Biala (Poland); (<b>g</b>) Perugia (Italy); (<b>h</b>) Plauen (Germany); (<b>i</b>) Orléans (France); (<b>j</b>) Poznaň (Poland); (<b>k</b>) Guimares (Portugal); (<b>l</b>) Osnabrück (Germany); (<b>m</b>) Augsburg (Germany); (<b>n</b>) Aviles (Spain); (<b>o</b>) Ljubljana (Slovenia); (<b>p</b>) Lübeck (Germany); (<b>q</b>) Enschede (Netherlands); (<b>r</b>) Oviedo (Spain); (<b>s</b>) Glogow (Poland); (<b>t</b>) Maastricht (Netherlands); (<b>u</b>) České Budějovice (Czech Republic); (<b>v</b>) Hradec Králové (Czech Republic); (<b>w</b>) Crawley (UK); (<b>x</b>) Örebro (Sweden).</p> Full article ">Figure A1 Cont.
<p>Pairs of similar cities according to land use produced by hierarchical clustering. (<b>a</b>) Modena (Italy); (<b>b</b>) Parma (Italy); (<b>c</b>) Perpignan (France); (<b>d</b>) Plovdiv (Bulgaria); (<b>e</b>) Basel (Switzerland); (<b>f</b>) Bielsko-Biala (Poland); (<b>g</b>) Perugia (Italy); (<b>h</b>) Plauen (Germany); (<b>i</b>) Orléans (France); (<b>j</b>) Poznaň (Poland); (<b>k</b>) Guimares (Portugal); (<b>l</b>) Osnabrück (Germany); (<b>m</b>) Augsburg (Germany); (<b>n</b>) Aviles (Spain); (<b>o</b>) Ljubljana (Slovenia); (<b>p</b>) Lübeck (Germany); (<b>q</b>) Enschede (Netherlands); (<b>r</b>) Oviedo (Spain); (<b>s</b>) Glogow (Poland); (<b>t</b>) Maastricht (Netherlands); (<b>u</b>) České Budějovice (Czech Republic); (<b>v</b>) Hradec Králové (Czech Republic); (<b>w</b>) Crawley (UK); (<b>x</b>) Örebro (Sweden).</p> Full article ">Figure A1 Cont.
<p>Pairs of similar cities according to land use produced by hierarchical clustering. (<b>a</b>) Modena (Italy); (<b>b</b>) Parma (Italy); (<b>c</b>) Perpignan (France); (<b>d</b>) Plovdiv (Bulgaria); (<b>e</b>) Basel (Switzerland); (<b>f</b>) Bielsko-Biala (Poland); (<b>g</b>) Perugia (Italy); (<b>h</b>) Plauen (Germany); (<b>i</b>) Orléans (France); (<b>j</b>) Poznaň (Poland); (<b>k</b>) Guimares (Portugal); (<b>l</b>) Osnabrück (Germany); (<b>m</b>) Augsburg (Germany); (<b>n</b>) Aviles (Spain); (<b>o</b>) Ljubljana (Slovenia); (<b>p</b>) Lübeck (Germany); (<b>q</b>) Enschede (Netherlands); (<b>r</b>) Oviedo (Spain); (<b>s</b>) Glogow (Poland); (<b>t</b>) Maastricht (Netherlands); (<b>u</b>) České Budějovice (Czech Republic); (<b>v</b>) Hradec Králové (Czech Republic); (<b>w</b>) Crawley (UK); (<b>x</b>) Örebro (Sweden).</p> Full article ">
22 pages, 2822 KiB  
Article
Supporting Disaster Resilience Spatial Thinking with Serious GeoGames: Project Lily Pad
by Brian Tomaszewski, Amy Walker, Emily Gawlik, Casey Lane, Scott Williams, Deborah Orieta, Claudia McDaniel, Matthew Plummer, Anushka Nair, Nicolas San Jose, Nathan Terrell, Kyle Pecsok, Emma Thomley, Erin Mahoney, Emily Haberlack and David Schwartz
ISPRS Int. J. Geo-Inf. 2020, 9(6), 405; https://doi.org/10.3390/ijgi9060405 - 22 Jun 2020
Cited by 7 | Viewed by 5996
Abstract
The need for improvement of societal disaster resilience and response efforts was evident after the destruction caused by the 2017 Atlantic hurricane season. We present a novel conceptual framework for improving disaster resilience through the combination of serious games, geographic information systems (GIS), [...] Read more.
The need for improvement of societal disaster resilience and response efforts was evident after the destruction caused by the 2017 Atlantic hurricane season. We present a novel conceptual framework for improving disaster resilience through the combination of serious games, geographic information systems (GIS), spatial thinking, and disaster resilience. Our framework is implemented via Project Lily Pad, a serious geogame based on our conceptual framework, serious game case studies, interviews and real-life experiences from 2017 Hurricane Harvey survivors in Dickinson, TX, and an immersive hurricane-induced flooding scenario. The game teaches a four-fold set of skills relevant to spatial thinking and disaster resilience, including reading a map, navigating an environment, coding verbal instructions, and determining best practices in a disaster situation. Results of evaluation of the four skills via Project Lily Pad through a “think aloud” study conducted by both emergency management novices and professionals revealed that the game encouraged players to think spatially, can help build awareness for disaster response scenarios, and has potential for real-life use by emergency management professionals. It can be concluded from our results that the combination of serious games, geographic information systems (GIS), spatial thinking, and disaster resilience, as implemented via Project Lily Pad and our evaluation results, demonstrated the wide range of possibilities for using serious geogames to improve disaster resilience spatial thinking and potentially save lives when disasters occur. Full article
(This article belongs to the Special Issue Gaming and Geospatial Information)
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<p>A diagram showing how ideas from the four focused concepts examined through the scoping literature review were used to develop a research conceptual framework.</p> Full article ">Figure 2
<p>A prototypical scene from the 2017 emergency response to Hurricane Harvey in Dickinson, Texas, where flood survivors are being evacuated from their homes. The Project Lily Pad game discussed in this paper is based on events like show in in this figure as a means to use serious GIS games to build disaster resilience spatial thinking skills. This image was provided from the collection of an emergency management professional from Galveston County, Texas; identities have been concealed for privacy.</p> Full article ">Figure 3
<p>A diagram showing how spatial concepts and thinking are able to enhance one’s spatial abilities and skills.</p> Full article ">Figure 4
<p>(<b>a</b>–<b>h</b>) Overview of Project Lily Pad missions: (<b>a</b>) first, the player acts as a first responder delivering supplies to key locations in the city before water levels reach flood stage; (<b>b</b>) throughout the game, the player uses several map layers as seen in this reference map; (<b>c</b>) layer of markers that players apply to the map themselves to highlight important landmarks; (<b>d</b>) the player also learns how to adjust to the needs of vulnerable populations during the pre-flood stage of the game; (<b>e</b>) as a flood occurs, they act as a member of a volunteer-run disaster relief organization, rescuing people from their flooded homes via boat; (<b>f</b>) the game player rescues people by taking them to “lily pads”, a term created by emergency responders in Galveston county during Hurricane Harvey in 2017, for areas of higher elevation minimally impacted by flooding; (<b>g</b>) game players can also review a digital elevation model (DEM) view of the flood for spatial awareness about the relationships between flood waters and topography; (<b>h</b>) after each rescue mission, the player receives feedback on the quality of the lily pads they have chosen based on the geographic information they were provided.</p> Full article ">Figure 5
<p>Diagram showing how Project Lily Pad helps improve the four spatial abilities of an individual that are needed to deal with a disaster.</p> Full article ">Figure 6
<p>Summary of pre- and post-game experience questionnaire results.</p> Full article ">Figure 7
<p>Think aloud evaluation of Project Lily Pad in Galveston County, Texas, with an emergency management professional who was an actual responder to Hurricane Harvey in 2017.</p> Full article ">Figure 8
<p>The Project Emergency Operations Center (EOC) prototype game that was developed based on results and experiences from developing project Lily Pad.</p> Full article ">
22 pages, 3102 KiB  
Article
GroupSeeker: An Applicable Framework for Travel Companion Discovery from Vast Trajectory Data
by Ruihong Yao, Fei Wang, Shuhui Chen and Shuang Zhao
ISPRS Int. J. Geo-Inf. 2020, 9(6), 404; https://doi.org/10.3390/ijgi9060404 - 20 Jun 2020
Cited by 2 | Viewed by 2586
Abstract
The popularity of mobile locate-enabled devices and Location Based Service (LBS) generates massive spatio-temporal data every day. Due to the close relationship between behavior patterns and movement trajectory, trajectory data mining has been applied in numerous fields to find the behavior pattern. Among [...] Read more.
The popularity of mobile locate-enabled devices and Location Based Service (LBS) generates massive spatio-temporal data every day. Due to the close relationship between behavior patterns and movement trajectory, trajectory data mining has been applied in numerous fields to find the behavior pattern. Among them, discovering traveling companions is one of the most fundamental techniques in these areas. This paper proposes a flexible framework named GroupSeeker for discovering traveling companions in vast real-world trajectory data. In the real-world data resource, it is significant to avoid the companion candidate omitting problem happening in the time-snapshot-slicing-based method. These methods do not work well with the sparse real-world data, which is caused by the equipment sampling failure or manual intervention. In this paper, a 5-stage framework including Data Preprocessing, Spatio-temporal Clustering, Candidate Voting, Pseudo-companion Filtering, and Group Merging is proposed to discover traveling companions. The framework even works well when there is a long time span during several days. The experiments result on two real-world data sources which offer massive amount of data subsets with different scale and different sampling frequencies show the effective and robustness of this framework. Besides, the proposed framework has a higher-efficiency performing when discovering satisfying companions over a long-term period. Full article
(This article belongs to the Special Issue Recent Trends in Location Based Services and Science)
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<p>An example of companion candidate omitting problem.</p> Full article ">Figure 2
<p>The framework of the entire processing.</p> Full article ">Figure 3
<p>The Detailed Process Example of Methodology.</p> Full article ">Figure 4
<p>Trajectory Clustering for Discovering Time and Location Potential Candiate.</p> Full article ">Figure 5
<p>(<b>a</b>,<b>b</b>) Overview of D2 [<a href="#B53-ijgi-09-00404" class="html-bibr">53</a>].</p> Full article ">Figure 6
<p>(<b>a</b>–<b>d</b>) Brief Contact and No-contact.</p> Full article ">Figure 7
<p>(<b>a</b>–<b>d</b>) Filtered Results.</p> Full article ">Figure 8
<p>(<b>a</b>,<b>b</b>) Typical results in <span class="html-italic">D2</span>.</p> Full article ">
14 pages, 5809 KiB  
Article
Village-Level Homestead and Building Floor Area Estimates Based on UAV Imagery and U-Net Algorithm
by Xueyan Zhang
ISPRS Int. J. Geo-Inf. 2020, 9(6), 403; https://doi.org/10.3390/ijgi9060403 - 20 Jun 2020
Cited by 8 | Viewed by 2852
Abstract
China’s rural population has declined markedly with the acceleration of urbanization and industrialization, but the area under rural homesteads has continued to expand. Proper rural land use and management require large-scale, efficient, and low-cost rural residential surveys; however, such surveys are time-consuming and [...] Read more.
China’s rural population has declined markedly with the acceleration of urbanization and industrialization, but the area under rural homesteads has continued to expand. Proper rural land use and management require large-scale, efficient, and low-cost rural residential surveys; however, such surveys are time-consuming and difficult to accomplish. Unmanned aerial vehicle (UAV) technology coupled with a deep learning architecture and 3D modelling can provide a potential alternative to traditional surveys for gathering rural homestead information. In this study, a method to estimate the village-level homestead area, a 3D-based building height model (BHM), and the number of building floors based on UAV imagery and the U-net algorithm was developed, and the respective estimation accuracies were found to be 0.92, 0.99, and 0.89. This method is rapid and inexpensive compared to the traditional time-consuming and costly household surveys, and, thus, it is of great significance to the ongoing use and management of rural homestead information, especially with regards to the confirmation of homestead property rights in China. Further, the proposed combination of UAV imagery and U-net technology may have a broader application in rural household surveys, as it can provide more information for decision-makers to grasp the current state of the rural socio-economic environment. Full article
(This article belongs to the Special Issue Unmanned Aerial Systems and Geoinformatics)
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<p>Study area: (<b>a</b>) Qimen county, Anhui province, (<b>b</b>) Jianfeng village, Qimen county. The red triangles in <a href="#ijgi-09-00403-f001" class="html-fig">Figure 1</a>b indicate household survey locations.</p> Full article ">Figure 2
<p>U-net architecture. Each blue box corresponds to a multi-channel feature map. The number of channels is denoted on top of the box. The x-y-size is provided at the lower left edge of the box. White boxes represent copied feature maps. The arrows denote the different operations.</p> Full article ">Figure 3
<p>Comparison of the unmanned aerial vehicle (UAV) image, ground truth, and U-net algorithm recognition results in indicative regions. (<b>a</b>) UAV red, green, and blue wavelength (RGB) images, (<b>b</b>) corresponding ground truth images (yellow for homesteads and white for other areas), and (<b>c</b>) results identified by the U-net algorithm (green for homestead and white for other areas).</p> Full article ">Figure 4
<p>Village-level estimates of the areas and spatial distributions of rural homesteads. (<b>a</b>) Ground truth of homesteads, and (<b>b</b>) identification results based on the U-net algorithm.</p> Full article ">Figure 5
<p>UAV-based estimates of the number of floors in rural buildings. (<b>a</b>) Digital terrain model (DTM) built based on the SfM method from the UAV images, (<b>b</b>) digital surface model (DSM) based on kriging interpolation with 633 control points, and (<b>c</b>) building height model (BHM) divided into different floors (unit: m).</p> Full article ">Figure 6
<p>Verification of the DSM with a scatter plot of the testing points of the DSM and DTM. The <span class="html-italic">x</span>- and <span class="html-italic">y</span>-coordinates represent the testing points of the DTM and DSM, respectively.</p> Full article ">Figure 7
<p>Consistency between the number of floors recorded in the household survey and the number of floors estimated with the proposed technique. The bars show the number of floors surveyed, while the scatter plot represents the calculated homestead floor heights. The black dots indicate that the estimated number of floors is consistent with the number of floors recorded in the survey, and the hollow dots indicate inconsistency. The blue, green, and gray regions in the background represent the number of floors (ground, two, and three) of the homestead, respectively.</p> Full article ">
14 pages, 16094 KiB  
Article
Exploring Urban Spatial Features of COVID-19 Transmission in Wuhan Based on Social Media Data
by Zhenghong Peng, Ru Wang, Lingbo Liu and Hao Wu
ISPRS Int. J. Geo-Inf. 2020, 9(6), 402; https://doi.org/10.3390/ijgi9060402 - 19 Jun 2020
Cited by 56 | Viewed by 18950
Abstract
During the early stage of the COVID-19 outbreak in Wuhan, there was a short run of medical resources, and Sina Weibo, a social media platform in China, built a channel for novel coronavirus pneumonia patients to seek help. Based on the geo-tagging Sina [...] Read more.
During the early stage of the COVID-19 outbreak in Wuhan, there was a short run of medical resources, and Sina Weibo, a social media platform in China, built a channel for novel coronavirus pneumonia patients to seek help. Based on the geo-tagging Sina Weibo data from February 3rd to 12th, 2020, this paper analyzes the spatiotemporal distribution of COVID-19 cases in the main urban area of Wuhan and explores the urban spatial features of COVID-19 transmission in Wuhan. The results show that the elderly population accounts for more than half of the total number of Weibo help seekers, and a close correlation between them has also been found in terms of spatial distribution features, which confirms that the elderly population is the group of high-risk and high-prevalence in the COVID-19 outbreak, needing more attention of public health and epidemic prevention policies. On the other hand, the early transmission of COVID-19 in Wuhan could be divide into three phrases: Scattered infection, community spread, and full-scale outbreak. This paper can help to understand the spatial transmission of COVID-19 in Wuhan, so as to propose an effective public health preventive strategy for urban space optimization. Full article
(This article belongs to the Special Issue GIS in Healthcare)
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<p>Time-series change histogram in the early stage of the epidemic.</p> Full article ">Figure 2
<p>Map of the study area in Wuhan, China: (<b>a</b>) the geographic location of the Wuhan, China; (<b>b</b>) the main urban area of Wuhan (MUA); and (<b>c</b>) administrative districts of Wuhan.</p> Full article ">Figure 3
<p>The distribution of Weibo COVID-19 help seeking records.</p> Full article ">Figure 4
<p>An example of Weibo help-seeking information.</p> Full article ">Figure 5
<p>Spatial distribution of Wuhan Weibo help-seeking data.</p> Full article ">Figure 6
<p>Spatial distribution of base stations in the main urban area of Wuhan.</p> Full article ">Figure 7
<p>Histogram of patient age distribution of case data and Weibo data.</p> Full article ">Figure 8
<p>The number of infectors reported in each of the Weibo records.</p> Full article ">Figure 9
<p>Spatial distribution of Weibo help seekers in the main urban area of Wuhan.</p> Full article ">Figure 10
<p>Timing chart of COVID-19 infection of Weibo records and total infector reported</p> Full article ">Figure 11
<p>Kernel density analysis: (<b>a</b>) COVID-19 cases by Weibo data; (<b>b</b>) population density; and (<b>c</b>) the elderly population density generated by mobile phone data.</p> Full article ">Figure 12
<p>Spatial distribution of help seekers from December 20th, 2019 to January 22nd, 2020: (<b>a</b>) before January 18th, 2020; (<b>b</b>) from January 19th to 20th, 2020; and (<b>c</b>) from January 21st to 22nd, 2020.</p> Full article ">Figure 13
<p>Spatial distribution of help seekers from January 23rd to 28th, 2020: (<b>a</b>) from January 23rd to 24th, 2020; (<b>b</b>) from January 25th to 26th, 2020; and (<b>c</b>) from January 27th to 28th, 2020.</p> Full article ">Figure 14
<p>Spatial distribution of help seekers from January 29th to February 10th, 2020: (<b>a</b>) from January 29th to 30th, 2020; (<b>b</b>) from January 31st to February 1st, 2020; (<b>c</b>) from February 2nd to 3rd, 2020; (<b>d</b>) from February 4th to 5th, 2020; (<b>e</b>) from February 6th to 7th, 2020; and (<b>f</b>) from February 8th to 10th, 2020.</p> Full article ">
13 pages, 3705 KiB  
Article
Spatial Dimension of Unemployment: Space-Time Analysis Using Real-Time Accessibility in Czechia
by Pavlína Netrdová and Vojtěch Nosek
ISPRS Int. J. Geo-Inf. 2020, 9(6), 401; https://doi.org/10.3390/ijgi9060401 - 18 Jun 2020
Cited by 9 | Viewed by 3304
Abstract
This paper focuses on the analysis of unemployment data in Czechia on a very detailed spatial structure and yearly, extended time series (2002–2019). The main goal of the study was to examine the spatial dimension of disparities in regional unemployment and its evolutionary [...] Read more.
This paper focuses on the analysis of unemployment data in Czechia on a very detailed spatial structure and yearly, extended time series (2002–2019). The main goal of the study was to examine the spatial dimension of disparities in regional unemployment and its evolutionary tendencies on a municipal level. To achieve this goal, global and local spatial autocorrelation methods were used. Besides spatial and space-time analyses, special attention was given to spatial weight matrix selection. The spatial weights were created according to real-time accessibilities between the municipalities based on the Czech road network. The results of spatial autocorrelation analyses based on network spatial weights were compared to the traditional distance-based spatial weights. Despite significant methodological differences between applied spatial weights, the resulting spatial pattern of unemployment proved to be very similar. Empirically, relative stability of spatial patterns of unemployment with only slow shift of differentiation from macro- to microlevels could be observed. Full article
(This article belongs to the Special Issue Spationomy—Spatial Exploration of Economic Data)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Example of neighboring units delimited using Euclidean distances and time accessibility. Source: own calculation. Note: The selected municipality, Soutice, is located close to the main highway, where the biggest differences between the Euclidean distances and time accessibility were expected.</p> Full article ">Figure 2
<p>Theil index decomposition and Moran’s <span class="html-italic">I</span> with 10–60 km distance-based spatial weights; unemployment between 2002 and 2019 in Czechia (municipal level). Note: Existing administrative divisions: districts (NUTS 3 = 14 units) and areas of municipalities with extended powers (AMEPs = 206 units).</p> Full article ">Figure 3
<p>Bivariate Moran’s <span class="html-italic">I</span>, unemployment between 2002 and 2019 in Czechia (municipal level, yearly data). Source: Ministry of Labor and Social Affairs, Labor Office of the Czech Republic; own calculation. Note: Moran’s <span class="html-italic">I</span> is calculated based on spatial weights with the fixed Euclidean distance for the threshold distance of 10 km. All values are statistically significant at a 1% significance level based on 999 permutations. The shade of color corresponds to the level of spatial autocorrelation. The values on the diagonal represent univariate Moran’s <span class="html-italic">I</span> for respective years.</p> Full article ">Figure 4
<p>Univariate and bivariate local indicators of spatial association (LISA) cluster maps for unemployment between 2002 and 2019 in Czechia (municipal level). Source: Ministry of Labor and Social Affairs, Labor Office of the Czech Republic; own calculation. Note: All LISA cluster maps are calculated for distance-based spatial weights with fixed Euclidean distance and a threshold distance of 10 km.</p> Full article ">Figure 5
<p>Moran’s <span class="html-italic">I</span> with network spatial weight matrix; unemployment between 2002 and 2019 in Czechia (municipal level). Source: Ministry of Labor and Social Affairs, Labor Office of the Czech Republic; own calculation.</p> Full article ">Figure 6
<p>The z-scores significance levels for spatial autocorrelation measured by Moran’s <span class="html-italic">I</span> with 10–68 km distance-based spatial weights for unemployment between 2002 and 2019 in Czechia (municipal level). Source: Ministry of Labor and Social Affairs, Labor Office of the Czech Republic; own calculation.</p> Full article ">
14 pages, 3900 KiB  
Communication
Terrain Analysis in Google Earth Engine: A Method Adapted for High-Performance Global-Scale Analysis
by José Lucas Safanelli, Raul Roberto Poppiel, Luis Fernando Chimelo Ruiz, Benito Roberto Bonfatti, Fellipe Alcantara de Oliveira Mello, Rodnei Rizzo and José A. M. Demattê
ISPRS Int. J. Geo-Inf. 2020, 9(6), 400; https://doi.org/10.3390/ijgi9060400 - 17 Jun 2020
Cited by 51 | Viewed by 9638
Abstract
Terrain analysis is an important tool for modeling environmental systems. Aiming to use the cloud-based computing capabilities of Google Earth Engine (GEE), we customized an algorithm for calculating terrain attributes, such as slope, aspect, and curvatures, for different resolution and geographical extents. The [...] Read more.
Terrain analysis is an important tool for modeling environmental systems. Aiming to use the cloud-based computing capabilities of Google Earth Engine (GEE), we customized an algorithm for calculating terrain attributes, such as slope, aspect, and curvatures, for different resolution and geographical extents. The calculation method is based on geometry and elevation values estimated within a 3 × 3 spheroidal window, and it does not rely on projected elevation data. Thus, partial derivatives of terrain are calculated considering the great circle distances of reference nodes of the topographic surface. The algorithm was developed using the JavaScript programming interface of the online code editor of GEE and can be loaded as a custom package. The algorithm also provides an additional feature for making the visualization of terrain maps with a dynamic legend scale, which is useful for mapping different extents: from local to global. We compared the consistency of the proposed method with an available but limited terrain analysis tool of GEE, which resulted in a correlation of 0.89 and 0.96 for aspect and slope over a near-global scale, respectively. In addition to this, we compared the slope, aspect, horizontal, and vertical curvature of a reference site (Mount Ararat) to their equivalent attributes estimated on the System for Automated Geospatial Analysis (SAGA), which achieved a correlation between 0.96 and 0.98. The visual correspondence of TAGEE and SAGA confirms its potential for terrain analysis. The proposed algorithm can be useful for making terrain analysis scalable and adapted to customized needs, benefiting from the high-performance interface of GEE. Full article
(This article belongs to the Special Issue Big Data Computing for Geospatial Applications)
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<p>A 3 × 3 spheroidal equal angular grid with linear geometries a, b, c, d, and f, and nine elevation nodes—adapted from [<a href="#B8-ijgi-09-00400" class="html-bibr">8</a>].</p> Full article ">Figure 2
<p>TAGEE modules for calculating terrain parameters, derivatives, and attributes.</p> Full article ">Figure 3
<p>Example of terrain attributes calculated from TAGEE package and 1 arc-second SRTM DEM, displayed for the near-global extent at the visualization level 3 (~20 km pixel resolution): horizontal curvature (<b>A</b>), vertical curvature (<b>B</b>), and Northernness (<b>C</b>).</p> Full article ">Figure 4
<p>3D visualizations of terrain attributes produced near Mount Ararat: slope, horizontal and vertical curvature from TAGEE (<b>A</b>,<b>C</b>,<b>E</b>, respectively) and SAGA GIS (<b>B</b>,<b>D</b>,<b>F</b>, respectively). 3D maps are displayed with a vertical exaggeration of 2.</p> Full article ">
21 pages, 55347 KiB  
Article
Evaluating the Influence of Urban Morphology on Urban Wind Environment Based on Computational Fluid Dynamics Simulation
by Chia-An Ku and Hung-Kai Tsai
ISPRS Int. J. Geo-Inf. 2020, 9(6), 399; https://doi.org/10.3390/ijgi9060399 - 17 Jun 2020
Cited by 24 | Viewed by 4715
Abstract
Due to urbanization around the world, people living in urban areas have been suffering from a series of negative effects caused by changes in urban microclimate, especially when it comes to urban heat islands (UHIs). To mitigate UHIs, management of urban wind environments [...] Read more.
Due to urbanization around the world, people living in urban areas have been suffering from a series of negative effects caused by changes in urban microclimate, especially when it comes to urban heat islands (UHIs). To mitigate UHIs, management of urban wind environments is increasingly considered as a crucial part of the process. Computational fluid dynamics (CFD) simulation of wind fields has become a prevailing method to explore the relationship between morphological factors and wind environment. However, most studies are focused on building scale and fail to reflect the effects of comprehensive planning. In addition, the combined influence of different morphological factors on wind environment is rarely discussed. Therefore, this study tries to explore the relationship between urban morphology and wind environment in a new-town area. CFD method was applied to simulate the wind field, and 11 scenarios based on criteria according to existing literature, planning regulations and local characteristics were developed. The simulation results from different scenarios show that the impact of the five selected factors on wind speeds was non-linear, and the impact varied significantly among different areas of the study region. Simulation of the differences in regional wind speeds among different planning scenarios can provide strong decision-making support. Full article
(This article belongs to the Special Issue The Applications of 3D-City Models in Urban Studies)
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<p>Study region.</p> Full article ">Figure 2
<p>Flowchart for scenario simulation.</p> Full article ">Figure 3
<p>Architecture of software model ENVI-met [<a href="#B27-ijgi-09-00399" class="html-bibr">27</a>].</p> Full article ">Figure 4
<p>Locations of the monitoring stations.</p> Full article ">Figure 5
<p>ENVI-met-simulated results and systematic sampling points.</p> Full article ">Figure 6
<p>Time series of validation data.</p> Full article ">Figure 7
<p>Examples of building model settings in simulation scenarios.</p> Full article ">Figure 8
<p>Simulated wind field in the initial scenario (height: 1.5 m).</p> Full article ">Figure 9
<p>Mean hourly wind speed during a 24-h period in each scenario.</p> Full article ">Figure 10
<p>Wind speed differences between the initial scenario and Scenario 6 (2 times versus 1.5 times the base FAR).</p> Full article ">Figure 11
<p>Distribution of simulated wind speeds in the initial scenario (<b>A</b>) versus Scenario 2 (<b>B</b>).</p> Full article ">Figure 12
<p>Distribution of simulated wind speeds in Scenario 2 (<b>A</b>) versus Scenario 3 (<b>B</b>).</p> Full article ">Figure 13
<p>Wind speed difference between each pair of scenarios with different coefficients of variation for building height. ((<b>A</b>): Initial scenario; (<b>B</b>): Scenario 2; (<b>C</b>): Scenario 3; (<b>D</b>): Scenario 6; (<b>E</b>): Scenario 8; (<b>F</b>): Scenario 9)</p> Full article ">
31 pages, 21772 KiB  
Article
Geointelligence against Illegal Deforestation and Timber Laundering in the Brazilian Amazon
by Franco Perazzoni, Paula Bacelar-Nicolau and Marco Painho
ISPRS Int. J. Geo-Inf. 2020, 9(6), 398; https://doi.org/10.3390/ijgi9060398 - 17 Jun 2020
Cited by 7 | Viewed by 4768 | Correction
Abstract
Due to the characteristics of the Southern Amazonas Mesoregion (Mesorregião Sul do Amazonas, MSA), conducting on-site surveys in all licensed forestry areas (Plano de Manejo Florestal, PMFS) is an impossible task. Therefore, the present investigation aimed to: (i) analyze the use of geointelligence [...] Read more.
Due to the characteristics of the Southern Amazonas Mesoregion (Mesorregião Sul do Amazonas, MSA), conducting on-site surveys in all licensed forestry areas (Plano de Manejo Florestal, PMFS) is an impossible task. Therefore, the present investigation aimed to: (i) analyze the use of geointelligence (GEOINT) techniques to support the evaluation of PMFS; and (ii) verify if the PMFS located in the MSA are being executed in accordance with Brazilian legislation. A set of twenty-two evaluation criteria were established. These were initially applied to a “standard” PMFS and subsequently replicated to a larger area of 83 PMFS, located in the MSA. GEOINT allowed for a better understanding of each PMFS, identifying illegal forestry activities and evidence of timber laundering. Among these results, we highlight the following evidences: (i) inconsistencies related to total transport time and prices declared to the authorities (70% of PMFS); (ii) volumetric information incompatible with official forest inventories and/or not conforming with Benford’s law (54% of PMFS); (iii) signs of exploitation outside the authorized polygon limits (51% of PMFS) and signs of clear-cutting (43% of PMFS); (iv) no signs of infrastructure compatible with licensed forestry (24% of PMFS); and (v) signs of exploitation prior to the licensing (19% of PMFS) and after the expiration of licensing (5%). Full article
(This article belongs to the Special Issue Using GIS to Improve (Public) Safety and Security)
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<p>Visual outline of the legal timber trade process in Brazil.</p> Full article ">Figure 2
<p>Visual outline of illegal forestry in Brazil (adapted from [<a href="#B6-ijgi-09-00398" class="html-bibr">6</a>,<a href="#B9-ijgi-09-00398" class="html-bibr">9</a>]).</p> Full article ">Figure 3
<p>Components of GEOINT (adapted from [<a href="#B16-ijgi-09-00398" class="html-bibr">16</a>]).</p> Full article ">Figure 4
<p>Flowchart of methodology.</p> Full article ">Figure 5
<p>Standard Plano de Manejo Florestal (PMFS) production units (data from [<a href="#B46-ijgi-09-00398" class="html-bibr">46</a>]).</p> Full article ">Figure 6
<p>General information on Mesorregião Sul do Amazonas (MSA) [data from 42]. (<b>a</b>) Location, (<b>b</b>) traded volume per year, (<b>c</b>) comparison of State of Amazonas vs. MSA, (<b>d</b>) origin of timber production in MSA.</p> Full article ">Figure 7
<p>Overview of MSA and PMFS licensed in the years 2014–2018. Yellow rectangles show the main PMFS concentration areas.</p> Full article ">Figure 8
<p>Dashboard for non-spatial data (original in Portuguese).</p> Full article ">Figure 9
<p>Distribution of first digits according to Benford’s law.</p> Full article ">Figure 10
<p>Volumes of timber obtained through SisDOF and RADAM vs. Benford’s law. (<b>a</b>) All transactions in the State of Amazonas in SisDOF (2014–2018), (<b>b</b>) RADAM data for MSA (m<sup>3</sup>/ha), (<b>c</b>) RADAM data for MSA (total volume/species).</p> Full article ">Figure 10 Cont.
<p>Volumes of timber obtained through SisDOF and RADAM vs. Benford’s law. (<b>a</b>) All transactions in the State of Amazonas in SisDOF (2014–2018), (<b>b</b>) RADAM data for MSA (m<sup>3</sup>/ha), (<b>c</b>) RADAM data for MSA (total volume/species).</p> Full article ">Figure 11
<p>SisDOF trade data of <span class="html-italic">Allantoma lineata</span> (2014–2018) vs. Benford’s law.</p> Full article ">Figure 12
<p>SisDOF trade data of <span class="html-italic">Tabebuia serratifolia</span> (2014–2018) vs. Benford’s law.</p> Full article ">Figure 13
<p>General overview of standard PMFS and near protected areas.</p> Full article ">Figure 14
<p>General overview of MSA. Black circles correspond to PMFS overlap with protected areas.</p> Full article ">Figure 15
<p>Presence of storage yards and roads during selective logging (NDVI) inside red polygons representing standard PMFS and its annual units of production. (<b>a</b>) scale bar: 8 km, (<b>b</b>) scale bar: 3 km.</p> Full article ">Figure 16
<p>Complete absence of infrastructure and signs of selective logging (NDVI) inside red polygon representing PMFS (FID 1791). (<b>a</b>) scale bar: 8 km, (<b>b</b>) scale bar: 3 km.</p> Full article ">Figure 17
<p>Red arrows point to deforestation (red areas) that occurred in APP of the standard PMFS (dashed polygons) between the years 1993 and 2004 (MCTU).</p> Full article ">Figure 18
<p>Deforestation in the PMFS (FID 3364). Inside and around the dashed polygon corresponding to the PMFS limits, there is a predominance of reddish hue, with a regular shape and smooth texture, compatible with bare soil.</p> Full article ">Figure 19
<p>Yellow rectangles indicate presence of yards, roads and selective cutting signs (NDVI) inside three PMFS (red polygons—FIDs 4888, 2987 and 4191) between August/2011 (<b>a</b>) and August/2013 (<b>b</b>), despite the SisDOF transactions starting only in June/2014.</p> Full article ">Figure 20
<p>Exploitation in the area between 2016 (<b>b</b>), 2017 (<b>c</b>) and 2018 (<b>d</b>), after the last DOF issue in 2015 (<b>a</b>). Yellow circles indicate areas of selective cut inside and around the PMFS polygon in red (FID 4572).</p> Full article ">Figure 21
<p>Yellow circles identify areas explored outside the authorized PMFS polygon (in red). (<b>a</b>) NDVI—2009 (~1 ha), (<b>b</b>) NDVI—2016 (~3 ha).</p> Full article ">Figure 22
<p>Exploitation carried out outside the limits of the polygonal. Yellow circles identify areas explored outside the authorized PMFS polygon in red (FIDs 4888, 2987, and 4191).</p> Full article ">Figure 23
<p>Monthly volume sold by FIDs 4999 (<b>a</b>) and 4936 (<b>b</b>).</p> Full article ">Figure 24
<p>Main species sold by FID 3662 and its prices.</p> Full article ">Figure 25
<p>Transport speed (FID 4034).</p> Full article ">
18 pages, 57039 KiB  
Article
Past and Present Practices of Topographic Base Map Database Update in Nepal
by Nimisha Wagle and Tri Dev Acharya
ISPRS Int. J. Geo-Inf. 2020, 9(6), 397; https://doi.org/10.3390/ijgi9060397 - 16 Jun 2020
Cited by 5 | Viewed by 16771
Abstract
Topographic Base Maps (TBMs) are those maps that portray ground relief as the form of contour lines and show planimetric details. Various other maps like geomorphological maps, contour maps, and land use planning maps are derived from topographical maps. In this constantly changing [...] Read more.
Topographic Base Maps (TBMs) are those maps that portray ground relief as the form of contour lines and show planimetric details. Various other maps like geomorphological maps, contour maps, and land use planning maps are derived from topographical maps. In this constantly changing world, the update of TBMs is indispensable. In Nepal, their update and maintenance are done by the Survey Department (SD) as a national mapping agency. This paper presents the history of topographical mapping and the reasons for the lack of updates. Currently, the SD is updating the TBM database using panchromatic and multispectral images from the Zi Yuan-3 (ZY-3) satellite with a resolution of 2.1 and 5.8 m, respectively. The updated methodology includes the orthorectification of images, the pansharpening of images, field data collection, digitization, change detection, and updating, the overlay of vector data and field verification, data quality control, and printing map production. A TBM in the Dang district of Nepal is presented as casework to show the changes in the area and issues faced during the update. Though the present digitizing procedure is time-consuming and labor-intensive, the use of high-resolution imagery has made mapping accurate and has produced high-quality maps. However, audit and automation can be introduced from the experiences of other countries for accurate and frequent updates of the TBM database in Nepal. Full article
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Graphical abstract
Full article ">Figure 1
<p>The coverage of different topographic mappings of Nepal during 1992–2001.</p> Full article ">Figure 2
<p>Flow chart of current Topographic Base Maps (TBM) database update adopted by the Survey Department of Nepal.</p> Full article ">Figure 3
<p>Location of the Dang district and the TBM sheet number 2782-03D.</p> Full article ">Figure 4
<p>Comparison of the new map with the old map, as well as a pansharpened image from satellite ZY-3 of November 2018 for various features.</p> Full article ">Figure 5
<p>The comparison of symbology in old and new TBMs.</p> Full article ">Figure A1
<p>The old TBM of sheet no. 2782-3D.</p> Full article ">Figure A2
<p>The new TBM of sheet no. 2782-3D.</p> Full article ">
27 pages, 2934 KiB  
Article
Sustainable Spatial and Temporal Development of Land Prices: A Case Study of Czech Cities
by Jaroslav Burian, Karel Macků, Jarmila Zimmermannová and Rostislav Nétek
ISPRS Int. J. Geo-Inf. 2020, 9(6), 396; https://doi.org/10.3390/ijgi9060396 - 16 Jun 2020
Cited by 1 | Viewed by 3432
Abstract
Only a limited number of studies have examined land price issues based on official land price maps. A very unique timeline of official land price maps (2006–2019) allowed research to be conducted on four Czech cities (Prague, Olomouc, Ostrava, and Zlín). The main [...] Read more.
Only a limited number of studies have examined land price issues based on official land price maps. A very unique timeline of official land price maps (2006–2019) allowed research to be conducted on four Czech cities (Prague, Olomouc, Ostrava, and Zlín). The main aim of the research was to describe the links between land price, land use types, and macroeconomic indicators, and to compare temporal changes of these links in four cities of different size, type, and structure by using spatial data processing and regression analysis. The results showed that the key statistically significant variable in all cities was population size. The effect of this variable was mostly positive, except for Ostrava, as an example of a developing city. The second statistically significant variable affecting land prices in each city was discount rate. The effect of other variables differed according to the city, its characteristics, and stage of economic development. We concluded that the development of land prices over time was slightly different between the studied cities and partially dependent on local spatial factors. Nevertheless, stagnation in 2010–2011, probably as a consequence of the global economic crisis in 2009, was observed in each city. Changes in the monitored cities could be seen from a spatial point of view in similar land price patterns. The ratio of land area with rising prices was very similar in each city (85%–92%). The highest land prices were typically in urban centers, but prices rose only gradually. A much more significant increase in prices occurred in each city in their peripheral residential areas. The results of this study can improve understanding of urban development and the economic and spatial aspects of sustainability in land price changes. Full article
(This article belongs to the Special Issue Spationomy—Spatial Exploration of Economic Data)
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<p>Study area—four selected cities within Czechia.</p> Full article ">Figure 2
<p>Land price median value development.</p> Full article ">Figure 3
<p>Parcel price changes in the monitored cities between 2006 and 2019.</p> Full article ">Figure 4
<p>Correlation matrix for the city of Prague. Used abbreviations: Price—Land Price, Inhab—Inhabitants, Unem—Unemployment Rate, Econs—Registered Economic Subjects, Flatst—Started Flats, Flatfi—Finished Flats, Disc—Discount Rate, Inc—Household Income, Time—year, GDP—GDP</p> Full article ">Figure 5
<p>Correlation matrix for the city of Ostrava.</p> Full article ">Figure 6
<p>Correlation matrix for the city of Olomouc.</p> Full article ">Figure 7
<p>Absolute land prices in 2006 and relative land prices changes between 2006 and 2019 (a different scale was used for absolute values in Prague, as follows: 0–3000–6000–9000–15,000 and over).</p> Full article ">Figure 8
<p>Land price related to selected land use types.</p> Full article ">
27 pages, 8587 KiB  
Article
Mapping Submerged Aquatic Vegetation along the Central Vietnamese Coast Using Multi-Source Remote Sensing
by Tran Ngoc Khanh Ni, Hoang Cong Tin, Vo Trong Thach, Cédric Jamet and Izuru Saizen
ISPRS Int. J. Geo-Inf. 2020, 9(6), 395; https://doi.org/10.3390/ijgi9060395 - 16 Jun 2020
Cited by 8 | Viewed by 5830
Abstract
Submerged aquatic vegetation (SAV) in the Khanh Hoa (Vietnam) coastal area plays an important role in coastal communities and the marine ecosystem. However, SAV distribution varies widely, in terms of depth and substrate types, making it difficult to monitor using in-situ measurement. Remote [...] Read more.
Submerged aquatic vegetation (SAV) in the Khanh Hoa (Vietnam) coastal area plays an important role in coastal communities and the marine ecosystem. However, SAV distribution varies widely, in terms of depth and substrate types, making it difficult to monitor using in-situ measurement. Remote sensing can help address this issue. High spatial resolution satellites, with more bands and higher radiometric sensitivity, have been launched recently, including the Vietnamese Natural Resources, Environment, and Disaster Monitoring Satellite (VNREDSat-1) (V1) sensor from Vietnam, launched in 2013. The objective of the study described here was to establish SAV distribution maps for South-Central Vietnam, particularly in the Khanh Hoa coastal area, using Sentinel-2 (S2), Landsat-8, and V1 imagery, and then to assess any changes to SAV over the last ten years, using selected historical data. The satellite top-of-atmosphere signals were initially converted to radiance, and then corrected for atmospheric effects. This treated signal was then used to classify Khanh Hoa coastal water substrates, and these classifications were evaluated using 101 in-situ measurements, collected in 2017 and 2018. The results showed that the three satellites could provide high accuracy, with Kappa coefficients above 0.84, with V1 achieving over 0.87. Our results showed that, from 2008 to 2018, SAV acreage in Khanh Hoa was reduced by 74.2%, while gains in new areas compensated for less than half of these losses. This is the first study to show the potential for using V1 and S2 data to assess the distribution status of SAV in Vietnam, and its outcomes will contribute to the conservation of SAV beds, and to the sustainable exploitation of aquatic resources in the Khanh Hoa coastal area. Full article
(This article belongs to the Special Issue Advances in Geospatial Technologies in Monitoring Blue Carbon Ecosystems Using Multiple Source Remote Sensing and GIS Data)
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<p>Study area with submerged aquatic vegetation (SAV) assessment sites.</p> Full article ">Figure 2
<p>Survey sites along the Khanh Hoa coast, with pictures of various substrates (the blue lines in the ocean are isobaths).</p> Full article ">Figure 3
<p>Process flow chart for establishing SAV distribution and temporal change mapping.</p> Full article ">Figure 4
<p>Several linear logarithm relationships of reflectance spectra between band <span class="html-italic">i</span> and band <span class="html-italic">j</span> from VNREDSat-1 imagery: (<b>a</b>) band 1 and band 2; (<b>b</b>) band 1 and band 3; (<b>c</b>) band 1 and band 4; (<b>d</b>) band 2 and band 4; band 3 and band 4.</p> Full article ">Figure 5
<p>SAV distribution maps along the Khanh Hoa coast, created using data from three satellites: (<b>a</b>) Landsat-8 (2018), (<b>b</b>) Sentinel-2 (2019), and (<b>c</b>) VNREDSat-1 (2017).</p> Full article ">Figure 6
<p>SAV distribution maps for Van Phong Bay, using data from three satellites: (<b>a</b>) Landsat-8 (2018), (<b>b</b>) Sentinel-2 (2019), (<b>c</b>) VNREDSat-1 (2017).</p> Full article ">Figure 7
<p>SAV distribution maps for Nha Phu Lagoon, using data from three satellites: (<b>a</b>) Landsat-8 (2018), (<b>b</b>) Sentinel-2 (2019), (<b>c</b>) VNREDSat-1 (2017).</p> Full article ">Figure 8
<p>SAV distribution maps for Nha Trang Bay, using data from three satellites: (<b>a</b>) Landsat-8 (2018), (<b>b</b>) Sentinel-2 (2019), (<b>c</b>) VNREDSat-1 (2017).</p> Full article ">Figure 9
<p>SAV distribution maps for Thuy Trieu Lagoon, using data from three satellites: (<b>a</b>) Landsat-8 (2018), (<b>b</b>) Sentinel-2 (2019), (<b>c</b>) VNREDSat-1 (2017).</p> Full article ">Figure 10
<p>SAV distribution maps for Cam Ranh Bay, using data from three satellites: (<b>a</b>) Landsat-8 (2018), (<b>b</b>) Sentinel-2 (2019), (<b>c</b>) VNREDSat-1 (2017).</p> Full article ">Figure 11
<p>SAV change map in the Khanh Hoa coastal area for the period 2008–2018. Estimates were developed using data from Landsat-5 (2008) and Landsat-8 (2018).</p> Full article ">Figure 12
<p>Examples of point and polygon SAV classifications using Landsat 8, Sentinel-2, and VNREDSat-1 imagery from the Khanh Hoa coastal area. The imagery depicts SAV areas in (<b>a</b>) Van Phong Bay, and (<b>b</b>) My Giang, Nha Phu Lagoon.</p> Full article ">
29 pages, 10016 KiB  
Article
Measuring Accessibility of Healthcare Facilities for Populations with Multiple Transportation Modes Considering Residential Transportation Mode Choice
by Xinxin Zhou, Zhaoyuan Yu, Linwang Yuan, Lei Wang and Changbin Wu
ISPRS Int. J. Geo-Inf. 2020, 9(6), 394; https://doi.org/10.3390/ijgi9060394 - 16 Jun 2020
Cited by 31 | Viewed by 5316
Abstract
Accessibility research of healthcare facilities is developing towards multiple transportation modes (MTM), which are influenced by residential transportation choices and preferences. Due to differences in travel impact factors such as traffic conditions, origin location, distance to the destination, and economic cost, residents’ daily [...] Read more.
Accessibility research of healthcare facilities is developing towards multiple transportation modes (MTM), which are influenced by residential transportation choices and preferences. Due to differences in travel impact factors such as traffic conditions, origin location, distance to the destination, and economic cost, residents’ daily travel presents different residential transportation mode choices (RTMC). The purpose of our study was to measure the spatial accessibility of healthcare facilities based on MTM considering RTMC (MTM-RTMC). We selected the gravity two-step floating catchment area method (G2SFCA) as a fundamental model. Through the single transportation mode (STM), MTM, and MTM-RTMC, three aspects used to illustrate and redesign the G2SFCA, we obtained the MTM-RTMC G2SFCA model that integrates RTMC probabilities and the travel friction coefficient. We selected Nanjing as the experimental area, used route planning data of four modes (including driving, walking, public transportation, and bicycling) from a web mapping platform, and applied the three models to pediatric clinic services to measure accessibility. The results show that the MTM-RTMC mechanism is to make up for the traditional estimation of accessibility, which loses sight of the influence of residential transportation choices. The MTM-RTMC mechanism that provides a more realistic and reliable way can generalize to major accessibility models and offers preferable guidance for policymakers. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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<p>Schematic diagram of multiple transportation modes-residential transportation mode choices (MTM-RTMC). The RTMC probabilities, which mainly depend on different transportation modes’ travel impedance (<a href="#ijgi-09-00394-f001" class="html-fig">Figure 1</a>a), illustrate that different transportation modes from an origin location <span class="html-italic">i</span> (demand location) to destination location <span class="html-italic">j</span> (supply location) have different probabilities <span class="html-italic">w</span>(<span class="html-italic">m<sub>i</sub></span>) of being chosen (<a href="#ijgi-09-00394-f001" class="html-fig">Figure 1</a>b).</p> Full article ">Figure 2
<p>Schematic effect of different travel friction coefficients <span class="html-italic">β</span> in the line chart.</p> Full article ">Figure 3
<p>The RTMC theoretical model diagram.</p> Full article ">Figure 4
<p>The RTMC probability calculation schematic diagram.</p> Full article ">Figure 5
<p>The MTM trajectories of two selected empirical origin–destination pair (OD) flows. <a href="#ijgi-09-00394-f005" class="html-fig">Figure 5</a>a is short trip from Yujinli Community to NCHGZ, and <a href="#ijgi-09-00394-f005" class="html-fig">Figure 5</a>b is long distance trip from Yujinli Community to YHANMU.</p> Full article ">Figure 6
<p>The study area.</p> Full article ">Figure 7
<p>MTM time and distance from different street blocks to Nanjing Children’s Hospital of Guangzhou Office (NCHGZ) (high-resolution figure can be seen from <a href="https://figshare.com/s/7fc7a00e868a9c71ac37" target="_blank">https://figshare.com/s/7fc7a00e868a9c71ac37</a>).</p> Full article ">Figure 8
<p>The spatial distribution map of Nanjing’s child population (high-resolution figure can be seen from <a href="https://figshare.com/s/7fc7a00e868a9c71ac37" target="_blank">https://figshare.com/s/7fc7a00e868a9c71ac37</a>).</p> Full article ">Figure 9
<p>A variance bar chart of <span class="html-italic">V<sub>j</sub></span>.</p> Full article ">Figure 10
<p>The accessibility results of the three G2SFCA models (STM, MTM, and MTM-RTMC) (high-resolution figure can be seen from <a href="https://figshare.com/s/7fc7a00e868a9c71ac37" target="_blank">https://figshare.com/s/7fc7a00e868a9c71ac37</a>).</p> Full article ">Figure 11
<p>Different distance decay functions.</p> Full article ">
21 pages, 6006 KiB  
Article
National-Scale Landslide Susceptibility Mapping in Austria Using Fuzzy Best-Worst Multi-Criteria Decision-Making
by Meisam Moharrami, Amin Naboureh, Thimmaiah Gudiyangada Nachappa, Omid Ghorbanzadeh, Xudong Guan and Thomas Blaschke
ISPRS Int. J. Geo-Inf. 2020, 9(6), 393; https://doi.org/10.3390/ijgi9060393 - 16 Jun 2020
Cited by 28 | Viewed by 4052
Abstract
Landslides are one of the most detrimental geological disasters that intimidate human lives along with severe damages to infrastructures and they mostly occur in the mountainous regions across the globe. Landslide susceptibility mapping (LSM) serves as a key step in assessing potential areas [...] Read more.
Landslides are one of the most detrimental geological disasters that intimidate human lives along with severe damages to infrastructures and they mostly occur in the mountainous regions across the globe. Landslide susceptibility mapping (LSM) serves as a key step in assessing potential areas that are prone to landslides and could have an impact on decreasing the possible damages. The application of the fuzzy best-worst multi-criteria decision-making (FBWM) method was applied for LSM in Austria. Further, the role of employing a few numbers of pairwise comparisons on LSM was investigated by comparing the FBWM and Fuzzy Analytical Hierarchical Process (FAHP). For this study, a wide range of data was sourced from the Geological Survey of Austria, the Austrian Land Information System, Humanitarian OpenStreetMap Team, and remotely sensed data were collected. We used nine conditioning factors that were based on the previous studies and geomorphological characteristics of Austria, such as elevation, slope, slope aspect, lithology, rainfall, land cover, distance to drainage, distance to roads, and distance to faults. Based on the evaluation of experts, the slope conditioning factor was chosen as the best criterion (highest impact on LSM) and the distance to roads was considered as the worst criterion (lowest impact on LSM). LSM was generated for the region based on the best and worst criterion. The findings show the robustness of FBWM in landslide susceptibility mapping. Additionally, using fewer pairwise comparisons revealed that the FBWM can obtain higher accuracy as compared to FAHP. The finding of this research can help authorities and decision-makers to provide effective strategies and plans for landslide prevention and mitigation at the national level. Full article
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<p>The study area.</p> Full article ">Figure 2
<p>The triangular fuzzy set.</p> Full article ">Figure 3
<p>The proposed methodology for Fuzzy Analytical hierarchical process (FAHP).</p> Full article ">Figure 4
<p>Land susceptibility map for Austria using the FBWM model.</p> Full article ">Figure 5
<p>Land susceptibility map for Austria using FAHP model.</p> Full article ">Figure 6
<p>Success Rate (SR) and Prediction Rate (PR) curves for the FAHP and FBWM models. (<b>a</b>). SR- area under the curve (SR-AUC) for the FAHP; (<b>b</b>). SR-AUC for FBWM; (<b>c</b>). PR-AUC for the FAHP; and, (<b>d</b>). PR-AUC for FBWM.</p> Full article ">Figure 7
<p>Comparison of FBWM-FAHP.</p> Full article ">Figure 8
<p>Spatial location of overestimations, in relationship with the imbalanced classes.</p> Full article ">Figure 9
<p>Spatial location of underestimations, in relationship with the imbalanced classes.</p> Full article ">
22 pages, 8382 KiB  
Article
Mission Flight Planning of RPAS for Photogrammetric Studies in Complex Scenes
by José Miguel Gómez-López, José Luis Pérez-García, Antonio Tomás Mozas-Calvache and Jorge Delgado-García
ISPRS Int. J. Geo-Inf. 2020, 9(6), 392; https://doi.org/10.3390/ijgi9060392 - 16 Jun 2020
Cited by 18 | Viewed by 4849
Abstract
This study describes a new approach to Remotely Piloted Aerial Systems (RPAS) photogrammetric mission flight planning. In this context, we have identified different issues appearing in complex scenes or difficulties caused by the project requirements in order to establish those functions or tools [...] Read more.
This study describes a new approach to Remotely Piloted Aerial Systems (RPAS) photogrammetric mission flight planning. In this context, we have identified different issues appearing in complex scenes or difficulties caused by the project requirements in order to establish those functions or tools useful for resolving them. This approach includes the improvement of some common photogrammetric flight operations and the proposal of new flight schemas for some scenarios and practical cases. Some examples of these specific schemas are the combined flight (which includes characteristics of a classical block flight and a corridor flight in only one mission) and a polygon extrusion mode to be used for buildings and vertical objects, according to the International Committee of Architectural Photogrammetry (CIPA) recommendations. In all cases, it is very important to allow a detailed control of the flight and image parameters, such as the ground sample distance (GSD) variation, scale, footprints, coverage, and overlaps, according to the Digital Elevation Models (DEMs) available for the area. In addition, the application could be useful for quality control of other flights (or flight planning). All these new functions and improvements have been implemented in a software developed in order to make RPAS photogrammetric mission planning easier. The inclusion of new flight typologies supposes a novelty with respect to other available applications. The application has been tested using several cases including different types of flights. The results obtained in the quality parameters of flights (coverage and GSD variation) have demonstrated the viability of our new approach in supporting other photogrammetric procedures. Full article
(This article belongs to the Special Issue Unmanned Aerial Systems and Geoinformatics)
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<p>Examples of RPAS mission planners: (<b>a</b>) Astec Navigator using OpenStreetMap; (<b>b</b>) Mission Planner displaying Bing Aerial Image; (<b>c</b>) UgCS planning module using a block flight determined by a polygon limit using a Digital Elevation Model (DEM); (<b>d</b>) image footprints in Astec Navigator block flight; (<b>e</b>) image footprints in Astec Navigator corridor flight; and (<b>f</b>) circular flight planning in DJI GS Pro.</p> Full article ">Figure 2
<p>Schema of definition and application design.</p> Full article ">Figure 3
<p>(<b>a</b>) Forward ray casting method and (<b>b</b>) application to a real case using MFPlanner3D.</p> Full article ">Figure 4
<p>MFPlanner3D user’s interface: (<b>a</b>) project definition module and (<b>b</b>) flight planning module.</p> Full article ">Figure 5
<p>Example of conventional block flight on a sloped zone: (<b>a</b>) sloped zone delimited by polygonal line, (<b>b</b>) flight planned not considering the DEM, and (<b>c</b>) flight planned considering the DEM (with the best estimation of the mean ground sample distance (GSD)).</p> Full article ">Figure 6
<p>Examples of corridor flights: (<b>a</b>) corridor planning based on vertical photographs over the trace; (<b>b</b>) tilted images for a linear object; (<b>c</b>) tilted images of the linear object elevated (h’) from the ground.</p> Full article ">Figure 7
<p>Example of combined flight: (<b>a</b>) block and corridor combined flight and (<b>b</b>) real footprints obtained considering the DEM.</p> Full article ">Figure 8
<p>Example of polygon extrusion flight.</p> Full article ">Figure 9
<p>Influence of terrain morphology on block flight planning: (<b>a</b>) image locations using no-DEM option (flight height constant over terrain), (<b>b</b>) image locations using DEM option (variable flight height in strips considering terrain height), (<b>c</b>) and (<b>d</b>) number of recorded images (coverage map) for both projects, (<b>e</b>) and (<b>f</b>) GSD values for both projects, (<b>g</b>) and (<b>h</b>) frequency (number of cases) of GSD variation for both projects.</p> Full article ">Figure 10
<p>Linear case planning: (<b>a</b>) study zone, (<b>b</b>) position and orientation of planned images, (<b>c</b>) block flight, and (<b>d</b>) convergent flight.</p> Full article ">Figure 11
<p>Planning of the castle case: (<b>a</b>) block flight, (<b>b</b>) corridor flights considering 5° and 30° inclination angles, (<b>c</b>) polygon extrusion flight, and (<b>d</b>) combination of the three flights.</p> Full article ">Figure 12
<p>Mission planning of terrestrial photogrammetry using mast: (<b>a</b>) and (<b>b</b>) image locations (lateral and top views, respectively), (<b>c</b>) footprint map, and (<b>d</b>) images coverage map.</p> Full article ">Figure 13
<p>Control quality of a fixed-wing Remotely Piloted Aerial Systems (RPAS) flight: (<b>a</b>) and (<b>b</b>) image location of photographs (top and lateral views), (<b>c</b>) footprints map, (<b>d</b>) coverage map, (<b>e</b>) GSD map, and (<b>f</b>) direction and module (tilt angle) of optical axes.</p> Full article ">
29 pages, 49436 KiB  
Article
Assessment and Quantification of the Accuracy of Low- and High-Resolution Remote Sensing Data for Shoreline Monitoring
by Dionysios N. Apostolopoulos and Konstantinos G. Nikolakopoulos
ISPRS Int. J. Geo-Inf. 2020, 9(6), 391; https://doi.org/10.3390/ijgi9060391 - 15 Jun 2020
Cited by 29 | Viewed by 4061
Abstract
Τhe accuracy of low-resolution remote sensing data for monitoring shoreline evolution is the main issue that researchers have been trying to overcome in recent decades. The drawback of the Landsat satellite archive is its spatial resolution, which is appropriate only for low-scale mapping. [...] Read more.
Τhe accuracy of low-resolution remote sensing data for monitoring shoreline evolution is the main issue that researchers have been trying to overcome in recent decades. The drawback of the Landsat satellite archive is its spatial resolution, which is appropriate only for low-scale mapping. The present study investigates the potentialities and limitations of remote sensing data and GIS techniques in shoreline evolution modeling, with a focus on two major aspects: (a) assessing and quantifying the accuracy of low- and high-resolution remote sensing data for shoreline mapping; and (b) calculating the divergence in the forecasting of coastline evolution based on low- and high-resolution datasets. Shorelines derived from diachronic Landsat images are compared with the corresponding shorelines derived from high-spatial-resolution airphotos or Worldview-2 images. The accuracy of each dataset is assessed, and the possibility of forecasting shoreline evolution is investigated. Two sandy beaches, named Kalamaki and Karnari, which are located in Northwestern Peloponnese, Greece, are used as test sites. It is proved that the shorelines derived from the Landsat data present a displacement error of between 6 and 11 m. The specific data are not suitable for the shoreline forecasting procedure and should not be used in related studies, as they yield less accurate results for the two study areas in comparison with the high-resolution data. Full article
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<p>Map for the entire study area and both test sites.</p> Full article ">Figure 2
<p>Chart diagram of the study methodology.</p> Full article ">Figure 3
<p>Jagged edges of the Landsat-derived shoreline in comparison to that derived using the on-screen digitizing method (red line). (<b>a</b>) An example of MNDWI index. (<b>b</b>) An example of NDVI index. (<b>c</b>) An example of Landsat multispectral image derived through HCS fusion algorithm. (<b>d</b>) Overlapping of digitized shorelines.</p> Full article ">Figure 4
<p>Shorelines of 2018 overlapping at a high scale based on (<b>a</b>) high- and (<b>b</b>) low-resolution images for Kalamaki beach. Red cycles demonstrate areas where the deviation in the low-resolution data is obvious in high-scale mapping.</p> Full article ">Figure 4 Cont.
<p>Shorelines of 2018 overlapping at a high scale based on (<b>a</b>) high- and (<b>b</b>) low-resolution images for Kalamaki beach. Red cycles demonstrate areas where the deviation in the low-resolution data is obvious in high-scale mapping.</p> Full article ">Figure 5
<p>Shorelines of 2018 overlapping at a high scale based on high- and low-resolution images for Karnari beach. Red cycles demonstrate areas where the deviation in the low-resolution data is obvious in high-scale mapping.</p> Full article ">Figure 5 Cont.
<p>Shorelines of 2018 overlapping at a high scale based on high- and low-resolution images for Karnari beach. Red cycles demonstrate areas where the deviation in the low-resolution data is obvious in high-scale mapping.</p> Full article ">Figure 6
<p>Shoreline change envelope (SCE) rates of 1945–2008 shorelines based on high-resolution data for (<b>a</b>) the Kalamaki and (<b>b</b>) Karnari coasts.</p> Full article ">Figure 7
<p>Transects where the SCE rates of the 1945–2008 shorelines based on high-resolution data on the Karnari coast are over 40 m.</p> Full article ">Figure 8
<p>Shorelines of 2018 overlapping at a low scale for (<b>a</b>) Kalamaki and (<b>b</b>) Karnari beaches, based on both high- (<b>a</b>,<b>b</b>) and low-resolution (<b>c</b>,<b>d</b>) images.</p> Full article ">Figure 9
<p>SCE statistical rates from low- and high-resolution vectorized shorelines in 2018 for Kalamaki beach.</p> Full article ">Figure 9 Cont.
<p>SCE statistical rates from low- and high-resolution vectorized shorelines in 2018 for Kalamaki beach.</p> Full article ">Figure 10
<p>SCE statistical rates from low- and high-resolution vectorized shorelines in 2018 for Karnari beach.</p> Full article ">Figure 10 Cont.
<p>SCE statistical rates from low- and high-resolution vectorized shorelines in 2018 for Karnari beach.</p> Full article ">Figure 11
<p>End point rate (EPR) rates were computed for the 2008–2018 period from both low- and high-resolution data using the 2008 and 2018 “actual” shorelines and the 2018 “forecasted” shorelines for Kalamaki beach.</p> Full article ">Figure 11 Cont.
<p>End point rate (EPR) rates were computed for the 2008–2018 period from both low- and high-resolution data using the 2008 and 2018 “actual” shorelines and the 2018 “forecasted” shorelines for Kalamaki beach.</p> Full article ">Figure 12
<p>EPR rates computed for the 2008–2018 period from both the low- and high-resolution data using the 2008 and 2018 “actual” shorelines and the 2018 “forecasted” shorelines for Karnari beach.</p> Full article ">Figure 12 Cont.
<p>EPR rates computed for the 2008–2018 period from both the low- and high-resolution data using the 2008 and 2018 “actual” shorelines and the 2018 “forecasted” shorelines for Karnari beach.</p> Full article ">
17 pages, 11929 KiB  
Article
Mining Subsidence Prediction by Combining Support Vector Machine Regression and Interferometric Synthetic Aperture Radar Data
by Lichun Sui, Fei Ma and Nan Chen
ISPRS Int. J. Geo-Inf. 2020, 9(6), 390; https://doi.org/10.3390/ijgi9060390 - 15 Jun 2020
Cited by 15 | Viewed by 2837
Abstract
Mining subsidence is time-dependent and highly nonlinear, especially in the Loess Plateau region in Northwestern China. As a consequence, and mainly in building agglomerations, the structures can be damaged severely during or after underground extraction, with risks to human life. In this paper, [...] Read more.
Mining subsidence is time-dependent and highly nonlinear, especially in the Loess Plateau region in Northwestern China. As a consequence, and mainly in building agglomerations, the structures can be damaged severely during or after underground extraction, with risks to human life. In this paper, we propose an approach based on a combination of a differential interferometric synthetic aperture radar (DInSAR) technique and a support vector machine (SVM) regression algorithm optimized by grid search (GS-SVR) to predict mining subsidence in a timely and cost-efficient manner. We consider five Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR) images encompassing the Dafosi coal mine area in Binxian and Changwu counties, Shaanxi Province. The results show that the subsidence predicted by the proposed InSAR and GS-SVR approach is consistent with the Global Positioning System (GPS) measurements. The maximum absolute errors are less than 3.1 cm and the maximum relative errors are less than 14%. The proposed approach combining DInSAR with GS-SVR technology can predict mining subsidence on the Loess Plateau of China with a high level of accuracy. This research may also help to provide disaster warnings. Full article
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<p>Geographical location of the NO.40301 working panel in Dafosi coal mine and Global Positioning System (GPS) stations with the corresponding Google Earth image as the background.</p> Full article ">Figure 2
<p>Differential interferometric synthetic aperture radar (DInSAR) coherence maps: (<b>a</b>) the period from 4 July 2007 to 19 August 2007; (<b>b</b>) the period from 19 August 2007 to 4 October 2007; (<b>c</b>) the period from 4 October 2007 to 19 November 2007; and (<b>d</b>) the period from 19 November 2007 to 4 January 2008.</p> Full article ">Figure 3
<p>Differential interferometry phase maps: (<b>a</b>) the period from 4 July 2007 to 19 August 2007; (<b>b</b>) the period from 19 August 2007 to 4 October 2007; (<b>c</b>) the period from 4 October 2007 to 19 November 2007; (<b>d</b>) the period from 19 November 2007 to 4 January 2008.</p> Full article ">Figure 4
<p>Cumulative subsidence map.</p> Full article ">Figure 5
<p>Subsidence curves and prediction results along the strike-line.</p> Full article ">Figure 6
<p>Subsidence curves and prediction results along the dip-line.</p> Full article ">Figure 7
<p>Error distribution of prediction results.</p> Full article ">
17 pages, 7434 KiB  
Article
Urban Geological 3D Modeling Based on Papery Borehole Log
by Xinyu Zhang, Junqiang Zhang, Yiping Tian, Zhilong Li, Yi Zhang, Lirui Xu and Song Wang
ISPRS Int. J. Geo-Inf. 2020, 9(6), 389; https://doi.org/10.3390/ijgi9060389 - 12 Jun 2020
Cited by 20 | Viewed by 6697
Abstract
Borehole log is important data for urban geological 3D modeling. Most of the current borehole logs are stored in a papery form. The construction of a smart city puts forward requirements for the automatic and intelligent 3D modeling of urban geology. However, it [...] Read more.
Borehole log is important data for urban geological 3D modeling. Most of the current borehole logs are stored in a papery form. The construction of a smart city puts forward requirements for the automatic and intelligent 3D modeling of urban geology. However, it is difficult to extract the information from the papery borehole log quickly. What is more, it is unreliable to rely entirely on automated algorithms for modeling without artificial participation, but there is no effective way to integrate geological knowledge into 3D geological modeling currently. Therefore, it is necessary to research how to use existing papery borehole logs efficiently. To overcome the above obstacles, we designed a method that combines structural analysis and layout understanding to extract information from the borehole log. Then, the knowledge-driven three-dimensional geological modeling is proposed based on dynamic profiles. With these methods, the papery borehole log can be converted into structured data which can be used for data analysis directly, and geological knowledge can be integrated into the process of 3D geological modeling. The 3D geological modeling of Xinyang City based on a papery borehole log has been taken as an example to verify the feasibility of the method. Full article
(This article belongs to the Special Issue Geo-Enriched Data Modeling & Mining)
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<p>Example of old a papery borehole log with some non-vertical table segmentation points.</p> Full article ">Figure 2
<p>The CAD (Computer Aided Design) engineering drawings with table crossing lines.</p> Full article ">Figure 3
<p>3D geological modeling flow based on a papery borehole log.</p> Full article ">Figure 4
<p>Automatic extraction flow of borehole log information.</p> Full article ">Figure 5
<p>Method and flow of cell positioning.</p> Full article ">Figure 6
<p>Training process of information extraction model.</p> Full article ">Figure 7
<p>(<b>a</b>) The descending curve of the loss value on the train set. (<b>b</b>) The rising curve of the accuracy on the test set.</p> Full article ">Figure 8
<p>The binding framework for the 3D model and 2D profiles.</p> Full article ">Figure 9
<p>Calculating and determining the position of the pinch-out control point.</p> Full article ">Figure 10
<p>Connection rules of top border and bottom border of the lens.</p> Full article ">Figure 11
<p>Geological research area in Xinyang City.</p> Full article ">Figure 12
<p>Processing of an original borehole log in Xinyang City.</p> Full article ">Figure 13
<p>(<b>a</b>) Side-view of stratum connection. (<b>b</b>) Schematic diagram of connecting borehole points.</p> Full article ">Figure 14
<p>The profiles of random side.</p> Full article ">Figure 15
<p>Exclusion of the erroneous stratigraphic body with a thickness of zero.</p> Full article ">Figure 16
<p>The final geological model of the old town in Xinyang City with five perspectives.</p> Full article ">
15 pages, 1708 KiB  
Article
A Map Is a Living Structure with the Recurring Notion of Far More Smalls than Larges
by Bin Jiang and Terry Slocum
ISPRS Int. J. Geo-Inf. 2020, 9(6), 388; https://doi.org/10.3390/ijgi9060388 - 11 Jun 2020
Cited by 5 | Viewed by 4623
Abstract
The Earth’s surface or any territory is a coherent whole or subwhole, in which the notion of “far more small things than large ones” recurs at different levels of scale ranging from the smallest of a couple of meters to the largest of [...] Read more.
The Earth’s surface or any territory is a coherent whole or subwhole, in which the notion of “far more small things than large ones” recurs at different levels of scale ranging from the smallest of a couple of meters to the largest of the Earth’s surface or that of the territory. The coherent whole has the underlying character called wholeness or living structure, which is a physical phenomenon pervasively existing in our environment and can be defined mathematically under the new third view of space conceived and advocated by Christopher Alexander: space is neither lifeless nor neutral, but a living structure capable of being more alive or less alive. This paper argues that both the map and the territory are a living structure, and that it is the inherent hierarchy of “far more smalls than larges” that constitutes the foundation of maps and mapping. It is the underlying living structure of geographic space or geographic features that makes maps or mapping possible, i.e., larges to be retained, while smalls to be omitted in a recursive manner (Note: larges and smalls should be understood broadly and wisely, in terms of not only sizes, but also topological connectivity and semantic meaning). Thus, map making is largely an objective undertaking governed by the underlying living structure, and maps portray the truth of the living structure. Based on the notion of living structure, a map can be considered to be an iterative system, which means that the map is the map of the map of the map, and so on endlessly. The word endlessly means continuous map scales between two discrete ones, just as there are endless real numbers between 1 and 2. The iterated map system implies that each of the subsequent small-scale maps is a subset of the single large-scale map, not a simple subset but with various constraints to make all geographic features topologically correct. Full article
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<p>(Color online) Geometric primitives versus geometrically meaningful entities. (Note: A street network is represented as a set of junctions or street segments (geometric primitives, which are not centers) (<b>a</b>), whereas it is more correctly perceived as a collection of named streets (geometrically meaningful entities, which are centers) (<b>b</b>), each of which is colored as one of the four hierarchical levels: blue for the least connected streets, red for the most connected street (only one), and yellow and turquoise for those between the most and the least connected. A curvilinear feature is usually represented as a set of line segments (geometric primitives, which are not centers) (<b>c</b>), but it is more correctly perceived as a collection of far more small bends than large ones (geometrically meaningful entities, which are centers) (<b>d</b>), because the notion of far more small bends than large ones occurs twice: (1) x<sub>1</sub> + x<sub>2</sub> + x<sub>3</sub> &gt; x<sub>4</sub> + x<sub>5</sub> + x<sub>6</sub> + x<sub>7</sub>, and (2) x<sub>1</sub> &gt; x<sub>2</sub> + x<sub>3</sub>).</p> Full article ">Figure 2
<p>(Color online) The ten fictitious cities and their interrelationship constitute a living structure. (Note: As a structural invariant of the central place theory model (Christaller 1933) [<a href="#B5-ijgi-09-00388" class="html-bibr">5</a>], the cluster of the ten cities (<b>a</b>) is composed of the largest city (<b>b</b>) bounded by the red square, surrounded by two middle-sized cities (<b>c</b>) separated by the green line and bounded by the green box, and further surrounded by seven smallest cities (<b>d</b>) separated by blue lines and bounded by the blue box, thus with three hierarchical levels, indicated by dot sizes and colors. Because of mutual relationship among the ten cities (<b>e</b>), each city has different degree of life, as indicated by the dot sizes (<b>f</b>)).</p> Full article ">Figure 3
<p>Illustration of the head/tail breaks as an iterative function. (Note: The data as a whole is recursively divided into the head (for those greater than the average) and the tail (for those less than the average). The whole or data is seen as an iterated system, i.e., the head of the head of the head and so on. For the sake of simplicity, we illustrate three iterations or four classes).</p> Full article ">Figure 4
<p>Generation (<b>a</b>) and generalization (<b>b</b>) of the Koch curve with the first four iterations (Note: Beginning with a segment of scale 1 (n = 0), it is divided into thirds, and the middle third is replaced by two equilaterals of a triangle, leading to four segments of scale 1/3 (n = 1). This division and replacement process continues for scales 1/9, and 1/27, leading respectively to 16 segments, and 64 segments (n = 2, and 3). This is the generation of the Koch curve, as shown in panel (<b>a</b>). On the other hand, the Koch curve (Level 0) can be generalized in a step-by-step fashion, as shown in <a href="#ijgi-09-00388-t003" class="html-table">Table 3</a>, resulting in the outcome in panel (<b>b</b>)).</p> Full article ">Figure 5
<p>(Color online) A living structure with four hierarchical levels of natural streets. (Note: The natural streets that are represented—on the surface—by geometrical details of locations, sizes, and directions (<b>a</b>) are transformed into the topology of the streets or living structure, in the deep sense, with far more less-connected streets than well-connected ones (<b>b</b>).</p> Full article ">Figure 6
<p>Composition II by Piet Mondrian (<b>a</b>) and its evolution from the empty square. (Note: It meets the minimum condition of being a living structure, and it is simple enough to illustrate how it is differentiated in a step-by-step fashion in panels (<b>b</b>–<b>e</b>), thus with a gradually increasing degree of life or beauty; there are far more smalls (4) than larges (1) from (<b>b</b>,<b>c</b>), and again far more smalls (6) than larges (4) from (<b>c</b>,<b>d</b>), so the ht-index is 3, which meets the condition of being a living structure. Thus both (<b>b</b>,<b>c</b>) are non-living structure, for their ht-index is less than 3. In addition, there is a violation of far more smalls (7) than larges (6) from (<b>d</b>,<b>e</b>), for 6 and 7 are more or less similar. If we consider the evolution in the opposite direction (from (<b>e</b>,<b>b</b>) then it can be viewed as a generalization process, very much like that of map generalization).</p> Full article ">
30 pages, 14167 KiB  
Article
Interdependent Healthcare Critical Infrastructure Analysis in a Spatiotemporal Environment: A Case Study
by Nivedita Nukavarapu and Surya Durbha
ISPRS Int. J. Geo-Inf. 2020, 9(6), 387; https://doi.org/10.3390/ijgi9060387 - 11 Jun 2020
Cited by 5 | Viewed by 2966
Abstract
During an urban flooding scenario, Healthcare Critical Infrastructure (HCI) represents a critical and essential resource. As the flood levels rise and the existing HCI facilities struggle to keep up with the pace, the under-preparedness of most urban cities to address this challenge becomes [...] Read more.
During an urban flooding scenario, Healthcare Critical Infrastructure (HCI) represents a critical and essential resource. As the flood levels rise and the existing HCI facilities struggle to keep up with the pace, the under-preparedness of most urban cities to address this challenge becomes evident. Due to the disruptions in the interdependent Critical Infrastructures (CI) network (i.e., water supply, communications, electricity, transportation, etc.), during an urban flooding event, the operations at the healthcare CI facilities are inevitably affected. Hence, there is a need to identify cascading CI failure scenarios to visualize the propagation of failure of one CI facility to another CI, which can impact vast geographical areas. The goal of this work is to develop an interdependent HCI simulation model in a spatiotemporal environment to understand the dynamics in real-time and model the propagation of cascading CI failures in an interdependent HCI network. The model is developed based on a real-world cascading CI failure case study on an interdependent HCI network during the flood disaster event in December 2015 at Chennai, TamilNadu, India. The interdependencies between the CI networks are modeled by using the Stochastic Colored Petri Net (SCPN) based modeling approach. SCPN is used to model a real-word process that occurs in parallel or concurrently. Furthermore, a geographic information system-based interface is integrated with the simulation model, to visualize the dynamic behavior of the interdependent HCI SCPN simulation model in a spatiotemporal environment. Such a dynamic simulation model can assist the decision-makers and emergency responders to rapidly simulate ‘what if’ kind of scenarios and consequently respond rapidly. Full article
(This article belongs to the Special Issue GIS in Healthcare)
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<p>Interdependencies between the different Critical Infrastructure facilities nodes.</p> Full article ">Figure 2
<p>Basic Petri net.</p> Full article ">Figure 3
<p>Example of a Timed Stochastic Colored Petri Net.</p> Full article ">Figure 4
<p>End to End Stochastic Colored Petri Net (SCPN) based interdependent Healthcare Critical Infrastructure (HCI) simulation model system in a spatiotemporal environment.</p> Full article ">Figure 5
<p>The proposed architectural framework for colored Petri net modeling for healthcare interdependencies during a flood event.</p> Full article ">Figure 6
<p>Temporal analysis of the SCPN interdependent HCI simulation model.</p> Full article ">Figure 7
<p>Inundated Healthcare facilities in Chennai during the Chennai Floods 2015. (3 December 2015 and 4 December 2015).</p> Full article ">Figure 8
<p>Fragility curve plot for Electrical Substation A (110/11 kV).</p> Full article ">Figure 9
<p>A snippet of the Flood Simulation Subnet.</p> Full article ">Figure 10
<p>Interdependent healthcare critical infrastructure network colored stochastic Petri net.</p> Full article ">Figure 11
<p>Healthcare critical infrastructure internal interdependencies.</p> Full article ">Figure 12
<p>A snippet of HCI interdependencies status monitoring subnet.</p> Full article ">Figure 13
<p>Stochastic Colored Petri net model-driven Geographic Information System.</p> Full article ">Figure 14
<p>Geo-visualization client interface For Interdependent HCI Petri Net Simulation Model - Chennai Case Study.</p> Full article ">Figure 15
<p>Transition HS1 in the SCPN model with geographical based constraints.</p> Full article ">Figure 16
<p>Ambulance using SCPN driven GIS-based Network operations.</p> Full article ">Figure 17
<p>An ambulance (red node) starting from Hospital A.</p> Full article ">Figure 18
<p>Ambulance (Red node) reached to safer Hospital B.</p> Full article ">
18 pages, 3242 KiB  
Article
Evaluating Temporal Approximation Methods Using Burglary Data
by Lukas Oswald and Michael Leitner
ISPRS Int. J. Geo-Inf. 2020, 9(6), 386; https://doi.org/10.3390/ijgi9060386 - 10 Jun 2020
Cited by 4 | Viewed by 4083
Abstract
Law enforcement is very interested in knowing when a crime has happened. Unfortunately, the occurrence time of a crime is often not exactly known. In such circumstances, estimating the most likely time that a crime has happened is crucial for spatio-temporal analysis. The [...] Read more.
Law enforcement is very interested in knowing when a crime has happened. Unfortunately, the occurrence time of a crime is often not exactly known. In such circumstances, estimating the most likely time that a crime has happened is crucial for spatio-temporal analysis. The main purpose of this research is to introduce two novel temporal approximation methods, termed retrospective temporal analysis (RTA) and extended retrospective temporal analysis (RTAext). Both methods are compared to six existing temporal approximation methods and subsequently evaluated in order to identify the method that can most accurately estimate the occurrence time of crimes. This research is conducted with 100,000+ burglary crimes from the city of Vienna, Austria provided by the Criminal Intelligence Service Austria, from 2009–2015. The RTA method assumes that crimes in the immediate past occur at very similar times as in the present and in the future. Historical crimes with accurately known time stamps can therefore be applied to estimate when crimes occur in the present/future. The RTAext method enhances one existing temporal approximation method, aoristicext, with probability values derived from historical crime data with accurately known time stamps. The results show that the RTA method performs superiorly to all other temporal approximation methods, including the novel RTAext method, in two out of the three crime types analyzed. Additionally, the RTAext method shows very good results that are similar to the best performing existing approximation methods. Full article
(This article belongs to the Special Issue Urban Crime Mapping and Analysis Using GIS)
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<p>An example of the results of four different naïve temporal approximation methods.</p> Full article ">Figure 1 Cont.
<p>An example of the results of four different naïve temporal approximation methods.</p> Full article ">Figure 2
<p>An example of the results of the aoristic and aoristic<sub>ext</sub> temporal approximation methods.</p> Full article ">Figure 3
<p>An example of the results of the extended retrospective temporal approximation (RTA<sub>ext</sub>) method.</p> Full article ">Figure 4
<p>Comparing observed (red dots) and estimated (green bars) occurrence times of apartment burglaries in Vienna, Austria between 2013–2015.</p> Full article ">Figure 5
<p>Comparing observed (red dots) and estimated (green bars) occurrence times of car burglaries in Vienna, Austria between 2013–2015.</p> Full article ">Figure 5 Cont.
<p>Comparing observed (red dots) and estimated (green bars) occurrence times of car burglaries in Vienna, Austria between 2013–2015.</p> Full article ">Figure 6
<p>Comparing observed (red dots) and estimated (green bars) occurrence times of house burglaries in Vienna, Austria between 2013–2015.</p> Full article ">Figure 6 Cont.
<p>Comparing observed (red dots) and estimated (green bars) occurrence times of house burglaries in Vienna, Austria between 2013–2015.</p> Full article ">
25 pages, 10353 KiB  
Article
A Thematic Similarity Network Approach for Analysis of Places Using Volunteered Geographic Information
by Xiaoyi Yuan, Andrew Crooks and Andreas Züfle
ISPRS Int. J. Geo-Inf. 2020, 9(6), 385; https://doi.org/10.3390/ijgi9060385 - 10 Jun 2020
Cited by 4 | Viewed by 4165
Abstract
The research presented in this paper proposes a thematic network approach to explore rich relationships between places. We connect places in networks through their thematic similarities by applying topic modeling to the textual volunteered geographic information (VGI) pertaining to the places. The network [...] Read more.
The research presented in this paper proposes a thematic network approach to explore rich relationships between places. We connect places in networks through their thematic similarities by applying topic modeling to the textual volunteered geographic information (VGI) pertaining to the places. The network approach enhances previous research involving place clustering using geo-textual information, which often simplifies relationships between places to be either in-cluster or out-of-cluster. To demonstrate our approach, we use as a case study in Manhattan (New York) that compares networks constructed from three different geo-textural data sources—TripAdvisor attraction reviews, TripAdvisor restaurant reviews, and Twitter data. The results showcase how the thematic similarity network approach enables us to conduct clustering analysis as well as node-to-node and node-to-cluster analysis, which is fruitful for understanding how places are connected through individuals’ experiences. Furthermore, by enriching the networks with geodemographic information as node attributes, we discovered that some low-income communities in Manhattan have distinctive restaurant cultures. Even though geolocated tweets are not always related to place they are posted from, our case study demonstrates that topic modeling is an efficient method to filter out the place-irrelevant tweets and therefore refining how of places can be studied. Full article
(This article belongs to the Special Issue Geo-Enriched Data Modeling & Mining)
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<p>An example of a TripAdvisor page and the highlights are the information scraped from the page.</p> Full article ">Figure 2
<p>Workflow from data input to the construction of the thematic similarity network and analysis (i.e., community detection and unique nodes discovery).</p> Full article ">Figure 3
<p>A stylized network demonstrating the process of community detection from a fully connected similarity network.</p> Full article ">Figure 4
<p>Cross validation results for community detection in three networks, modularity (<b>Left</b>) and number of one-node communities (<b>Right</b>).</p> Full article ">Figure 5
<p>The sizes of communities from the community detection results of the three networks.</p> Full article ">Figure 6
<p>Network visualization of all communities from the thematic similarity networks using Gephi [<a href="#B60-ijgi-09-00385" class="html-bibr">60</a>] Fruchterman–Reingold layout with major communities highlighted. Only the major communities are shown on the map for the sake of clarity. Major communities in Network visualization and mapping for each network are colored the same and thus the legend applies for both.</p> Full article ">Figure 7
<p>Dominant topics of all major communities in each thematic similarity network. Dominant topics are topics with coefficients equal or higher than 0.1.</p> Full article ">Figure 8
<p>Low-income communities highlighted and the label nodes represent the geodemographic type. (<b>a</b>) Network visualization of all communities and mapping of major communities (colored the same as <a href="#ijgi-09-00385-f006" class="html-fig">Figure 6</a>b). The node label represents their demographic classification. (<b>b</b>) Word cloud of topics in major communities. Topics of low-income communities are in visualized (<b>b</b>).</p> Full article ">Figure 9
<p>Visualization of the networks and nodes where large node size represents boundary nodes. Communities are colored the same as <a href="#ijgi-09-00385-f006" class="html-fig">Figure 6</a>.</p> Full article ">Figure 10
<p>Two examples of communities with boundary nodes and their respective topics. (<b>a</b>) An example from TripAdvisor attractions. (<b>b</b>) An example from TripAdvisor restaurants.</p> Full article ">
15 pages, 2880 KiB  
Article
Multifractal Characteristics of Seismogenic Systems and b Values in the Taiwan Seismic Region
by Chun Hui, Changxiu Cheng, Lixin Ning and Jing Yang
ISPRS Int. J. Geo-Inf. 2020, 9(6), 384; https://doi.org/10.3390/ijgi9060384 - 10 Jun 2020
Cited by 6 | Viewed by 2708
Abstract
Seismically active fault zones are complex natural systems and they exhibit multifractal correlation between earthquakes in space and time. In this paper, the seismicity of the Taiwan seismic region was studied through the multifractal characteristics of the spatial-temporal distribution of earthquakes from 1st [...] Read more.
Seismically active fault zones are complex natural systems and they exhibit multifractal correlation between earthquakes in space and time. In this paper, the seismicity of the Taiwan seismic region was studied through the multifractal characteristics of the spatial-temporal distribution of earthquakes from 1st January 1995 to 1st January 2019. We quantified the multifractal characteristics of Taiwan at different scales and defined them as ΔD values. Furthermore, we studied the relationship between the ΔD and b values, which signifies the average size distribution of those earthquakes. The results are as follows. (1) The temporal multifractal curve changes substantially before and after the strong earthquakes. (2) The maximum ΔD value of the seismic region in Taiwan occurs at depths of 0~9 km, indicating that geological structures and focal mechanisms is the most complex at these depths compared with other depths. (3) ΔD values for different regions range from 0.2~1.5, and b values range from 0.65~1.3, with a significant positive correlation between them (ΔD = 1.5 × b − 0.68). For this purpose, a statistical relationship is developed between b and ΔD values, and regional and temporal changes of these parameters are analyzed in order to reveal the potential of future earthquakes in the study region. Full article
(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)
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<p>Topographic map of the sea floor around Taiwan. The area outlined in pink is the study area.</p> Full article ">Figure 2
<p>Variations of magnitude completeness <span class="html-italic">M<sub>C</sub></span> value from 1995 to 2019 in the Taiwan region. Standard deviation (δM<sub>C</sub>) of the completeness (dashed lines) is also given. <span class="html-italic">M<sub>C</sub></span> is plotted for overlapping samples, each containing 200 events.</p> Full article ">Figure 3
<p>Cumulative number plots of the earthquakes versus time for all events with <span class="html-italic">M</span> ≥ 1.0 and declustered events with <span class="html-italic">M</span> ≥ 3 in the Taiwan region.</p> Full article ">Figure 4
<p>The change state of the fractal curve over time from 1998 to 2018 in the Taiwan region when <span class="html-italic">q</span> is taken from −5 to 5.</p> Full article ">Figure 5
<p>Magnitude distribution as a function of time for earthquakes occurred in the Taiwan region from 1995 to 2019.</p> Full article ">Figure 6
<p>Distribution of the seismic events in different depth layers.</p> Full article ">Figure 7
<p>Variations in the generalized dimension <span class="html-italic">D<sub>q</sub></span> with respect to q in different depth layers.</p> Full article ">Figure 8
<p>Study area and results from the tri-class seismic source model in different depth layers (Δ<span class="html-italic">D</span>) (<b>a</b>) represents the zones of different tectonic features; (<b>b</b>) represents the study area (Δ<span class="html-italic">D</span>); (<b>c</b>) represents the east and west seismic tectonic zones (Δ<span class="html-italic">D</span>), and (<b>d</b>) represents the different zones of tectonic features (Δ<span class="html-italic">D</span>). The shades of red indicate the value of Δ<span class="html-italic">D</span>, and gray indicates areas that do not have data).</p> Full article ">Figure 9
<p>Relationship between the <span class="html-italic">b</span> value of the Gutenberg–Richter relation and the Δ<span class="html-italic">D</span> of the multifractal. (<b>a</b>) represents the relationship of regression fit between <span class="html-italic">b</span> and Δ<span class="html-italic">D</span> values; (<b>b</b>) represents the specific range of <span class="html-italic">b</span> and Δ<span class="html-italic">D</span> values.</p> Full article ">
16 pages, 11256 KiB  
Article
Earth Observation and Artificial Intelligence for Improving Safety to Navigation in Canada Low-Impact Shipping Corridors
by René Chénier, Mesha Sagram, Khalid Omari and Adam Jirovec
ISPRS Int. J. Geo-Inf. 2020, 9(6), 383; https://doi.org/10.3390/ijgi9060383 - 10 Jun 2020
Cited by 4 | Viewed by 2884
Abstract
In 2014, through the World-Class Tanker Safety System (WCTSS) initiative, the Government of Canada launched the Northern Marine Transportation Corridors (NMTC) concept. The corridors were created as a strategic framework to guide Federal investments in marine transportation in the Arctic. With new government [...] Read more.
In 2014, through the World-Class Tanker Safety System (WCTSS) initiative, the Government of Canada launched the Northern Marine Transportation Corridors (NMTC) concept. The corridors were created as a strategic framework to guide Federal investments in marine transportation in the Arctic. With new government investment, under the Oceans Protection Plan (OPP), the corridors initiative, known as the Northern Low-Impact Shipping Corridors, will continue to be developed. Since 2016, the Canadian Hydrographic Service (CHS) has been using the corridors as a key layer in a geographic information system (GIS) model known as the CHS Priority Planning Tool (CPPT). The CPPT helps CHS prioritize its survey and charting efforts in Canada’s key traffic areas. Even with these latest efforts, important gaps in the surveys still need to be filled in order to cover the Canadian waterways. To help further develop the safety to navigation and improve survey mission planning, CHS has also been exploring new technologies within remote sensing. Under the Government Related Initiatives Program (GRIP) of the Canadian Space Agency (CSA), CHS has been investigating the potential use of Earth observation (EO) data to identify potential hazards to navigation that are not currently charted on CHS products. Through visual interpretation of satellite imagery, and automatic detection using artificial intelligence (AI), CHS identified several potential hazards to navigation that had previously gone uncharted. As a result, five notices to mariners (NTMs) were issued and the corresponding updates were applied to the charts. In this study, two AI approaches are explored using deep learning and machine learning techniques: the convolution neural network (CNN) and random forest (RF) classification. The study investigates the effectiveness of the two models in identifying shoals in Sentinel-2 and WorldView-2 satellite imagery. The results show that both CNN and RF models can detect shoals with accuracies ranging between 79 and 94% over two study sites; however, WorldView-2 images deliver results with higher accuracy and lower omission errors. The high processing times of using high-resolution imagery and training a deep learning model may not be necessary in order to quickly scan images for shoals; but training a CNN model with a large training set may lead to faster processing times without the need to train individual images. Full article
(This article belongs to the Special Issue Using GIS to Improve (Public) Safety and Security)
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<p>Northern Low-Impact Shipping Corridors within the Northern Canada Vessel Traffic Services Zone (NORDREG Zone).</p> Full article ">Figure 2
<p>Location of Study Site 1, near Puvirnituq, Quebec.</p> Full article ">Figure 3
<p>Outline of Study Site 1 in (<b>a</b>) Chart 5510 which indicates a large portion of the area is uncharted (showcased through whitespace on the chart), and (<b>b</b>) a Sentinel-2 image acquired on 21 August 2019 where shallow water is visible within the study area. ©2019, Copernicus.</p> Full article ">Figure 4
<p>Location of Study Site 2, near Taloyoak, Nunavut.</p> Full article ">Figure 5
<p>Outline of study site 2 near Taloyoak and the Northern Low-Impact Shipping Corridor in (<b>a</b>) Chart 7701 and (<b>b</b>) a WorldView-2 image acquired on 13 August 2017. ©2017, DigitalGlobe, Inc.</p> Full article ">Figure 6
<p>The original U-Net architecture [<a href="#B22-ijgi-09-00383" class="html-bibr">22</a>].</p> Full article ">Figure 7
<p>Example of notice to mariners (NTMs) issued by Canadian Hydrographic Service (CHS).</p> Full article ">Figure 8
<p>Example of a notice to mariners issued on CHS electronic navigational charts (ENC) (ENC #CA273274).</p> Full article ">Figure 9
<p>Location of potential corridor modification due to identified shoals.</p> Full article ">Figure 10
<p>Automatic detection results with (<b>a</b>) a WorldView-2 image of Study Site 1, (<b>b</b>) CNN deep learning WorldView-2 results, (<b>c</b>) random forest (RF) machine learning WorldView-2 results, (<b>d</b>) a Sentinel-2 image of Study Site 1, (<b>e</b>) CNN deep learning Sentinel results, and (<b>f</b>) RF machine learning Sentinel results. ©2017, DigitalGlobe and 2019, Copernicus.</p> Full article ">Figure 11
<p>Automatic detection results with (<b>a</b>) a WorldView-2 image of Study Site 2, (<b>b</b>) CNN deep learning WorldView-2 results, (<b>c</b>) RF machine learning WorldView-2 results, (<b>d</b>) a Sentinel-2 image of Study Site 2, (<b>e</b>) CNN deep learning Sentinel results, and (<b>f</b>) RF machine learning Sentinel results. ©2017, DigitalGlobe and 2018, Copernicus.</p> Full article ">
17 pages, 4516 KiB  
Article
Unfolding Events in Space and Time: Geospatial Insights into COVID-19 Diffusion in Washington State during the Initial Stage of the Outbreak
by Vaishnavi Thakar
ISPRS Int. J. Geo-Inf. 2020, 9(6), 382; https://doi.org/10.3390/ijgi9060382 - 10 Jun 2020
Cited by 21 | Viewed by 4760
Abstract
The world witnessed the COVID-19 pandemic in 2020. The first case of COVID-19 in the United States of America (USA) was confirmed on 21 January 2020, in Snohomish County in Washington State (WA). Following this, a rapid explosion of COVID-19 cases was observed [...] Read more.
The world witnessed the COVID-19 pandemic in 2020. The first case of COVID-19 in the United States of America (USA) was confirmed on 21 January 2020, in Snohomish County in Washington State (WA). Following this, a rapid explosion of COVID-19 cases was observed throughout WA and the USA. Lack of access to publicly available spatial data at finer scales has prevented scientists from implementing spatial analytical techniques to gain insights into the spread of COVID-19. Datasets were available only as counts at county levels. The spatial response to COVID-19 using coarse-scale publicly available datasets was limited to web mapping applications and dashboards to visualize infected cases from state to county levels only. This research approaches data availability issues by creating proxy datasets for COVID-19 using publicly available news articles. Further, these proxy datasets are used to perform spatial analyses to unfolding events in space and time and to gain insights into the spread of COVID-19 in WA during the initial stage of the outbreak. Spatial analysis of theses proxy datasets from 21 January to 23 March 2020, suggests the presence of a clear space–time pattern. From 21 January to 6 March, a strong presence of community spread of COVID-19 is observed only in close proximity of the outbreak source in Snohomish and King Counties, which are neighbors. Infections diffused to farther locations only after a month, i.e., 6 March. The space–time pattern of diffusion observed in this study suggests that implementing strict social distancing measures during the initial stage in infected locations can drastically help curb the spread to distant locations. Full article
(This article belongs to the Collection Spatial Components of COVID-19 Pandemic)
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<p>Status of worldwide confirmed cases of COVID-19 as of 26 March 2020 (Source: Johns Hopkins University [<a href="#B12-ijgi-09-00382" class="html-bibr">12</a>,<a href="#B13-ijgi-09-00382" class="html-bibr">13</a>]).</p> Full article ">Figure 2
<p>Washington State Department of Health (WSDH) announcement on 25 March 2020. (Source: WSDH).</p> Full article ">Figure 3
<p>Study area displaying counties and divided highways in Washington State overlaid on top of a satellite imagery base map.</p> Full article ">Figure 4
<p>Proxy COVID-19 locations overlaid on top of WSDH COVID-19 case counts per county: (<b>a</b>) 15 March 2020; (<b>b</b>) 23 March 2020.</p> Full article ">Figure 4 Cont.
<p>Proxy COVID-19 locations overlaid on top of WSDH COVID-19 case counts per county: (<b>a</b>) 15 March 2020; (<b>b</b>) 23 March 2020.</p> Full article ">Figure 5
<p>Proxy locations of COVID-19 cases in Snohomish and King Counties in WA from 21 January to 6 March 2020.</p> Full article ">Figure 6
<p>Proxy locations of all COVID-19 cases in WA: (<b>a</b>) 21 January to 4 March; (<b>b</b>) 6 March to 9 March (<b>c</b>) 10 March to 15 March; (<b>d</b>) 17 March to 23 March.</p> Full article ">Figure 7
<p>Proxy locations of two patients infected during international travel and their proximity to all COVID-19 proxy locations from 19 January 2020 to 23 March 2020.</p> Full article ">Figure 8
<p>Kernel density estimation for COVID -19 spread during four time intervals: (<b>a</b>) 21 January to 4 March (<b>b</b>) 6 March to 9 March; (<b>c</b>) 10 March to15 March; (<b>d</b>) 17 March to 23 March.</p> Full article ">Figure 8 Cont.
<p>Kernel density estimation for COVID -19 spread during four time intervals: (<b>a</b>) 21 January to 4 March (<b>b</b>) 6 March to 9 March; (<b>c</b>) 10 March to15 March; (<b>d</b>) 17 March to 23 March.</p> Full article ">Figure 9
<p>Spatial distribution of COVID-19 cases and the corresponding standard deviation ellipses (SDE) from 21 January to 6 March (green), 21 January to 15 March (yellow), and 21 January to 23 March (red).</p> Full article ">
20 pages, 4725 KiB  
Article
Uncorrelated Geo-Text Inhibition Method Based on Voronoi K-Order and Spatial Correlations in Web Maps
by Yufeng He, Yehua Sheng, Yunqing Jing, Yue Yin and Ahmad Hasnain
ISPRS Int. J. Geo-Inf. 2020, 9(6), 381; https://doi.org/10.3390/ijgi9060381 - 9 Jun 2020
Cited by 3 | Viewed by 2568
Abstract
Unstructured geo-text annotations volunteered by users of web map services enrich the basic geographic data. However, irrelevant geo-texts can be added to the web map, and these geo-texts reduce utility to users. Therefore, this study proposes a method to detect uncorrelated geo-text annotations [...] Read more.
Unstructured geo-text annotations volunteered by users of web map services enrich the basic geographic data. However, irrelevant geo-texts can be added to the web map, and these geo-texts reduce utility to users. Therefore, this study proposes a method to detect uncorrelated geo-text annotations based on Voronoi k-order neighborhood partition and auto-correlation statistical models. On the basis of the geo-text classification and semantic vector transformation, a quantitative description method for spatial autocorrelation was established by the Voronoi weighting method of inverse vicinity distance. The Voronoi k-order neighborhood self-growth strategy was used to detect the minimum convergence neighborhood for spatial autocorrelation. The Pearson method was used to calculate the correlation degree of the geo-text in the convergence region and then deduce the type of geo-text to be filtered. Experimental results showed that for given geo-text types in the study region, the proposed method effectively calculated the correlation between new geo-texts and the convergence region, providing an effective suggestion for preventing uncorrelated geo-text from uploading to the web map environment. Full article
(This article belongs to the Special Issue Advances in Computational Approaches for Spatial Analysis and Modeling)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Overall flow of the proposed method.</p> Full article ">Figure 2
<p>Flow of geo-text annotation classification.</p> Full article ">Figure 3
<p>Flow of proposed Correlated Geo-text Detection Algorithm (CGD).</p> Full article ">Figure 4
<p>Voronoi k-order adjacent model.</p> Full article ">Figure 5
<p>Voronoi k-order neighborhoods: (<b>a</b>) <span class="html-italic">vn</span>(1), (<b>b</b>), <span class="html-italic">vn</span>(2), and (<b>c</b>) and <span class="html-italic">vn</span>(3).</p> Full article ">Figure 6
<p>Voronoi k-order inverse distance weighting.</p> Full article ">Figure 7
<p>Geo-text annotations in (<b>a</b>) a single-type neighborhood and (<b>b</b>) a compound-type neighborhood.</p> Full article ">Figure 8
<p>Histogram correlation analysis between (<b>a</b>) <span class="html-italic">Hist</span>(<span class="html-italic">p<sub>m</sub></span>) and (<b>b</b>) and <span class="html-italic">Hist</span>(<span class="html-italic">p<sub>i</sub></span>).</p> Full article ">Figure 9
<p>Geo-text points on Baidu Map.</p> Full article ">Figure 10
<p>Chinese geo-text A (<b>a</b>), Chinese geo-text B (<b>b</b>) and Chinese geo-text C(<b>c</b>).</p> Full article ">Figure 11
<p>Geo-text classification results.</p> Full article ">Figure 12
<p>k-order neighborhood of geo-text A in (<b>a</b>) vn(1), (<b>b</b>) vn(2), (<b>c</b>) vn(3), (<b>d</b>) vn(4), (<b>e</b>) vn(5), and (<b>f</b>) <span class="html-italic">vn</span>(6) of P1.</p> Full article ">Figure 13
<p>Moran scatterplot of geo-text A in (<b>a</b>) <span class="html-italic">vn</span>(1), (<b>b</b>) <span class="html-italic">vn</span>(2), (<b>c</b>) <span class="html-italic">vn</span>(3), (<b>d</b>) <span class="html-italic">vn</span>(4), (<b>e</b>) <span class="html-italic">vn</span>(5), and (<b>f</b>) <span class="html-italic">vn</span>(6) of P1.</p> Full article ">Figure 14
<p>Trends of (<b>a</b>) <span class="html-italic">I</span>, (<b>b</b>) <span class="html-italic">z</span>, and (<b>c</b>) <span class="html-italic">p</span> values for points A, B, and C by <span class="html-italic">vn</span>(<span class="html-italic">k</span>).</p> Full article ">Figure 15
<p>High-value aggregation distribution in (<b>a</b>) <span class="html-italic">vn</span>(1), (<b>b</b>) <span class="html-italic">vn</span>(2), and (<b>c</b>) <span class="html-italic">vn</span>(3) of P7.</p> Full article ">Figure 16
<p>Low value aggregation distribution in (<b>a</b>) <span class="html-italic">vn</span>(1), (<b>b</b>) <span class="html-italic">vn</span>(2), and (<b>c</b>) <span class="html-italic">vn</span>(3) of P7.</p> Full article ">Figure 17
<p>(<b>a</b>) Convergence k value for A. (<b>b</b>) Correlated-to-uncorrelated ratio of geo-text A.</p> Full article ">Figure 18
<p>Result of the CGD algorithm, where the sizes of the circles represent the correlation degree.</p> Full article ">Figure 19
<p>Correlated-to-uncorrelated ratios of geo-texts A, B and C.</p> Full article ">
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