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Volume 7
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ISPRS Int. J. Geo-Inf., Volume 7, Issue 9 (September 2018) – 49 articles

Cover Story (view full-size image): The citizenship place network of cities is still hidden. Although many authors foresee the spatial structure of a city theoretically, its operationalization remains constrained in urban studies. This research contributes to this discussion through the exploratory examination of the geographical relationship between a sense of place and social capital at the collective and individual level. Using spatial data collected through a web-map-based survey, we found that a sense of place and social capital spatial dimensions had a non-disjointed relationship for approximately half of the participants and showed a spatial clustering when they were aggregated. This research wants to open up the agenda for further research into exploratory place-based geography studies and sets up common ground for other socially-oriented conceptualizations or applications. View this paper.
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13 pages, 2069 KiB  
Article
Using Remote Sensing to Analyse Net Land-Use Change from Conflicting Sustainability Policies: The Case of Amsterdam
by Mendel Giezen, Stella Balikci and Rowan Arundel
ISPRS Int. J. Geo-Inf. 2018, 7(9), 381; https://doi.org/10.3390/ijgi7090381 - 19 Sep 2018
Cited by 27 | Viewed by 9509
Abstract
In order to achieve the ambitious Sustainable Development Goal #11 (Sustainable Cities and Communities), an integrative approach is necessary. Complex outcomes such as sustainable cities are the product of a range of policies and drivers that are sometimes at odds with each other. [...] Read more.
In order to achieve the ambitious Sustainable Development Goal #11 (Sustainable Cities and Communities), an integrative approach is necessary. Complex outcomes such as sustainable cities are the product of a range of policies and drivers that are sometimes at odds with each other. Yet, traditional policy assessments often focus on specific ambitions such as housing, green spaces, etc., and are blind to the consequences of policy interactions. This research proposes the use of remote sensing technologies to monitor and analyse the resultant effects of opposing urban policies. In particular, we will look at the conflicting policy goals in Amsterdam between the policy to densify, on the one hand, and, on the other hand, goals of protecting and improving urban green space. We conducted an analysis to detect changes in land-uses within the urban core of Amsterdam, using satellite images from 2003 and 2016. The results indeed show a decrease of green space and an increase in the built-up environment. In addition, we reveal strong fragmentation of green space, indicating that green space is increasingly available in smaller patches. These results illustrate that the urban green space policies of the municipality appear insufficient to mitigate the negative outcomes of the city’s densification on urban green space. Additionally, we demonstrate how remote sensing can be a valuable instrument in investigating the net consequences of policies and urban developments that would be difficult to monitor through traditional policy assessments. Full article
(This article belongs to the Special Issue Geo-Information and the Sustainable Development Goals (SDGs))
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<p>Study area: Amsterdam within the ring zone. Data sources: [<a href="#B38-ijgi-07-00381" class="html-bibr">38</a>,<a href="#B39-ijgi-07-00381" class="html-bibr">39</a>]. Design by authors.</p> Full article ">Figure 2
<p>Land-use classification within Amsterdam core. Data Sources: Land-use classifications were made based on Worldview 2 satellite imagery for 2003 (resolution 0.46 m) and Quickbird imagery for 2016 (resolution 0.64 m). The data was provided by Digital Globe (2018) [<a href="#B44-ijgi-07-00381" class="html-bibr">44</a>]. All calculations made by the authors. * urban land includes built-up as well as non-green barren land.</p> Full article ">Figure 3
<p>Distribution and proximity to green space within Amsterdam ring zone. Data Sources: Land-use classifications were made based on Worldview 2 satellite imagery for 2003 (resolution 0.46 m) and Quickbird imagery for 2016 (resolution 0.64 m). The data was provided by Digital Globe (2018) [<a href="#B44-ijgi-07-00381" class="html-bibr">44</a>] All calculations made by the authors. * urban land includes built-up as well as non-green barren land.</p> Full article ">
7 pages, 2198 KiB  
Communication
Determining Optimal Video Length for the Estimation of Building Height through Radial Displacement Measurement from Space
by Andrew Plowright, Riccardo Tortini and Nicholas C. Coops
ISPRS Int. J. Geo-Inf. 2018, 7(9), 380; https://doi.org/10.3390/ijgi7090380 - 18 Sep 2018
Cited by 4 | Viewed by 3465
Abstract
We presented a methodology for estimating building heights in downtown Vancouver, British Columbia, Canada, using a high definition video (HDV) recorded from the International Space Station. We developed an iterative routine based on multiresolution image segmentation to track the radial displacement of building [...] Read more.
We presented a methodology for estimating building heights in downtown Vancouver, British Columbia, Canada, using a high definition video (HDV) recorded from the International Space Station. We developed an iterative routine based on multiresolution image segmentation to track the radial displacement of building roofs over the course of the HDV, and to predict the building heights using an ordinary least-squares regression model. The linear relationship between the length of the tracking vector and the height of the buildings was excellent (r2 ≤ 0.89, RMSE ≤ 8.85 m, p < 0.01). Notably, the accuracy of the height estimates was not improved considerably beyond 10 s of outline tracking, revealing an optimal video length for estimating the height or elevation of terrestrial features. HDVs are demonstrated to be a viable and effective data source for target tracking and building height prediction when high resolution imagery, spectral information, and/or topographic data from other sources are not available. Full article
(This article belongs to the Special Issue Cognitive Aspects of Human-Computer Interaction for GIS)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Example of radial displacement of buildings over the course of a high definition video from space.</p> Full article ">Figure 2
<p>Flowchart of the proposed rooftop tracking method.</p> Full article ">Figure 3
<p>Example of building rooftop tracking over the course of the high definition video (HDV). Outlines are colored according to the video time at which they were extracted.</p> Full article ">Figure 4
<p>Radial displacement of building rooftops in pixels, measured by tracking the movement of rooftop outlines over the course of the video. Initial building rooftop outlines appear as green polygons. Green targets represent the centroids of the tracked outlines’ final positions. Green vectors represent the radial displacement of the outlines. Red polygons represent three buildings that were lost during the tracking progress.</p> Full article ">Figure 5
<p>Temporal change in the relationship between buildings’ radial displacement and height above ground as measured by the r<sup>2</sup> of least-squares regression models.</p> Full article ">
20 pages, 10671 KiB  
Article
Feature Extraction and Selection of Sentinel-1 Dual-Pol Data for Global-Scale Local Climate Zone Classification
by Jingliang Hu, Pedram Ghamisi and Xiao Xiang Zhu
ISPRS Int. J. Geo-Inf. 2018, 7(9), 379; https://doi.org/10.3390/ijgi7090379 - 18 Sep 2018
Cited by 73 | Viewed by 6465
Abstract
The concept of the local climate zone (LCZ) has been recently proposed as a generic land-cover/land-use classification scheme. It divides urban regions into 17 categories based on compositions of man-made structures and natural landscapes. Although it was originally designed for temperature study, the [...] Read more.
The concept of the local climate zone (LCZ) has been recently proposed as a generic land-cover/land-use classification scheme. It divides urban regions into 17 categories based on compositions of man-made structures and natural landscapes. Although it was originally designed for temperature study, the morphological structure concealed in LCZs also reflects economic status and population distribution. To this end, global LCZ classification is of great value for worldwide studies on economy and population. Conventional classification approaches are usually successful for an individual city using optical remote sensing data. This paper, however, attempts for the first time to produce global LCZ classification maps using polarimetric synthetic aperture radar (PolSAR) data. Specifically, we first produce polarimetric features, local statistical features, texture features, and morphological features and compare them, with respect to their classification performance. Here, an ensemble classifier is investigated, which is trained and tested on already separated transcontinental cities. Considering the challenging global scope this work handles, we conclude the classification accuracy is not yet satisfactory. However, Sentinel-1 dual-Pol SAR data could contribute the classification for several LCZ classes. According to our feature studies, the combination of local statistical features and morphological features yields the best classification results with 61.8% overall accuracy (OA), which is 3% higher than the OA produced by the second best features combination. The 3% is considerably large for a global scale. Based on our feature importance analysis, features related to VH polarized data contributed the most to the eventual classification result. Full article
(This article belongs to the Special Issue Data Mining and Feature Extraction from Satellite Images and Point Cloud Data)
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<p>Description of LCZ classes. (adapted from [<a href="#B1-ijgi-07-00379" class="html-bibr">1</a>]).</p> Full article ">Figure 2
<p>World-wide distribution of selected 29 cities of interest. Red: cities for testing. Green: cities for training.</p> Full article ">Figure 3
<p>Processed Sentinel-1 Dual-Pol (VV and VH) data of 29 cities are shown in Pauli basis, overlapped with the labeled ground truth.</p> Full article ">Figure 4
<p>Flowchart of Sentinel-1 data preparation. Module with orange background indicates data downloading part. Module with blue background indicates data preprocessing part.</p> Full article ">Figure 5
<p>Morphological opening and closing operations on intensity of VH channel with a radius of 5, for the data of city Zurich. From left to right, top to bottom: VH channel in dB, opening, opening by reconstruction, closing, and closing by reconstruction.</p> Full article ">Figure 6
<p>Left figure presents the (OA) and kappa coefficient. Right figure illustrates producer accuracies of all 17 classes. These classes are: 1: Compact high-rise, 2: Compact mid-rise, 3: Compact low-rise, 4: Open high-rise, 5: Open mid-rise, 6: Open low-rise, 7: Light weight low-rise, 8: Large low-rise, 9: Sparsely built, 10: Heavy industry, 11: Dense trees, 12: Scattered trees, 13: Bush, scrub, 14: Low plants, 15: Bare rock or paved, 16: Bare soil or sand, 17: Water.</p> Full article ">Figure 7
<p>Classification evaluation using OA and kappa coefficient as the evaluation metrics.</p> Full article ">Figure 8
<p>Classification evaluation using OA and Kappa coefficient as the evaluation metrics.</p> Full article ">Figure 9
<p>Feature importance obtained by CCF.</p> Full article ">Figure 10
<p>Confusion matrix of the classification framework on feature combination <b>E</b>, which is the best feature combination in terms of OA. Numbers are reported in percentages.</p> Full article ">
26 pages, 12197 KiB  
Article
Beyond Spatial Proximity—Classifying Parks and Their Visitors in London Based on Spatiotemporal and Sentiment Analysis of Twitter Data
by Anna Kovacs-Györi, Alina Ristea, Ronald Kolcsar, Bernd Resch, Alessandro Crivellari and Thomas Blaschke
ISPRS Int. J. Geo-Inf. 2018, 7(9), 378; https://doi.org/10.3390/ijgi7090378 - 14 Sep 2018
Cited by 56 | Viewed by 10035
Abstract
Parks are essential public places and play a central role in urban livability. However, traditional methods of investigating their attractiveness, such as questionnaires and in situ observations, are usually time- and resource-consuming, while providing less transferable and only site-specific results. This paper presents [...] Read more.
Parks are essential public places and play a central role in urban livability. However, traditional methods of investigating their attractiveness, such as questionnaires and in situ observations, are usually time- and resource-consuming, while providing less transferable and only site-specific results. This paper presents an improved methodology of using social media (Twitter) data to extract spatial and temporal patterns of park visits for urban planning purposes, along with the sentiment of the tweets, focusing on frequent Twitter users. We analyzed the spatiotemporal park visiting behavior of more than 4000 users for almost 1700 parks, examining 78,000 tweets in London, UK. The novelty of the research is in the combination of spatial and temporal aspects of Twitter data analysis, applying sentiment and emotion extraction for park visits throughout the whole city. This transferable methodology thereby overcomes many of the limitations of traditional research methods. This study concluded that people tweeted mostly in parks 3–4 km away from their center of activity and they were more positive than elsewhere while doing so. In our analysis, we identified four types of parks based on their visitors’ spatial behavioral characteristics, the sentiment of the tweets, and the temporal distribution of the users, serving as input for further urban planning-related investigations. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Overview of the data preprocessing workflow.</p> Full article ">Figure 2
<p>Frequency of tweet count per user.</p> Full article ">Figure 3
<p>Map of tweets and parks (input data sets of the analysis).</p> Full article ">Figure 4
<p>Methodology overview.</p> Full article ">Figure 5
<p>The illustration of how COM is interpreted (<b>a</b>) and how it is used to measure the average distance between COM and a park tweet (<b>b</b>).</p> Full article ">Figure 6
<p>(<b>A</b>) Average distance to each tweet from COM—all tweets; (<b>B</b>) Average distance to each park tweet from park COM; (<b>C</b>) Median distance to each tweet from COM—all tweets; (<b>D</b>) Median distance to each park tweet from park COM.</p> Full article ">Figure 7
<p>Frequency of average distances from COM (of all tweets) to park tweets.</p> Full article ">Figure 8
<p>Average distance from users’ COM to the park, aggregated on park level based on the tweets posted from a given park.</p> Full article ">Figure 9
<p>Medoid values of user clusters.</p> Full article ">Figure 10
<p>Park categories based on the spatial characteristics of their visitors’ behavior.</p> Full article ">Figure 11
<p>Percentage of sentiments and emotions for park tweets and non-park tweets ((<b>A</b>) all tweets considered in one step; (<b>B</b>) aggregated user-level values).</p> Full article ">Figure 12
<p>Overall sentiment scores in parks with at least 100 tweets.</p> Full article ">Figure 13
<p>Temporal distribution of tweet frequency ((<b>A</b>) yearly; (<b>B</b>) weekly; (<b>C</b>) seasonal; and (<b>D</b>) hourly).</p> Full article ">Figure 14
<p>Park clusters according to visitors’ spatial behavior.</p> Full article ">Figure 15
<p>Proportion of positive tweets during the day on weekdays.</p> Full article ">Figure 16
<p>Proportion of positive tweets during the day at the weekends.</p> Full article ">Figure 17
<p>Proportion of fear tweets during the day, indicating weekday/weekend ratio as well.</p> Full article ">Figure 18
<p>Sub-clusters for parks (<b>A</b>) user clusters; (<b>B</b>) sentiment and emotion proportions; (<b>C</b>) daily pattern; (<b>D</b>) seasonal pattern).</p> Full article ">Figure 19
<p>Final park categories.</p> Full article ">
21 pages, 2611 KiB  
Article
Application of Industrial Risk Management Practices to Control Natural Hazards, Facilitating Risk Communication
by Jongook Lee and Dong Kun Lee
ISPRS Int. J. Geo-Inf. 2018, 7(9), 377; https://doi.org/10.3390/ijgi7090377 - 14 Sep 2018
Cited by 8 | Viewed by 5807
Abstract
Establishing a comprehensive management framework to manage the risk from natural hazards is challenging because of the extensive affected areas, uncertainty in predictions of natural disasters, and the involvement of various stakeholders. Applying risk management practices proven in the industrial sector can assist [...] Read more.
Establishing a comprehensive management framework to manage the risk from natural hazards is challenging because of the extensive affected areas, uncertainty in predictions of natural disasters, and the involvement of various stakeholders. Applying risk management practices proven in the industrial sector can assist systematic hazard identification and quantitative risk assessment for natural hazards, thereby promoting interactive risk communication to the public. The objective of this study is to introduce methods of studying risk commonly used in the process industry, and to suggest how such methods can be applied to manage natural disasters. In particular, the application of Hazard and Operability (HAZOP), Safety Integrated Level (SIL), and Quantitative Risk Analysis (QRA) was investigated, as these methods are used to conduct key studies in industry. We present case studies of the application of HAZOP to identify climate-related natural hazards, and of SIL and QRA studies that were performed to provide quantitative risk indices for landslide risk management. The analyses presented in this study can provide a useful framework for improving the risk management of natural hazards through establishing a more systematic context and facilitating risk communication. Full article
(This article belongs to the Special Issue Geospatial Methods and Tools for Natural Risk Management and Communications)
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Figure 1

Figure 1
<p>The steps used in hazard identification from derived deviations (adapted from [<a href="#B27-ijgi-07-00377" class="html-bibr">27</a>]).</p> Full article ">Figure 2
<p>(<b>a</b>) An example of the SIL graph method for determining the required risk reduction, (<b>b</b>) An example of the SIL matrix method for determining the required risk reduction (adapted from [<a href="#B37-ijgi-07-00377" class="html-bibr">37</a>]).</p> Full article ">Figure 3
<p>Changes in probability of failure on demand (PFD) across a year, and the average probability of failure on demand (PFDavg) (adapted from [<a href="#B43-ijgi-07-00377" class="html-bibr">43</a>]).</p> Full article ">Figure 4
<p>Landslide locations in the study area. The landslide inventory data were parts of the second and third rounds of data collection in Gangwon Province.</p> Full article ">Figure 5
<p>The steps of QRA, modified from chemical process QRA (adapted from [<a href="#B12-ijgi-07-00377" class="html-bibr">12</a>]).</p> Full article ">Figure 6
<p>The case study site and the results of TRIGRS modeling: (<b>a</b>) the site location; (<b>b</b>) topography of the site; (<b>c</b>) TRIGRS modeling result—200 mm for 48 h; (<b>d</b>) TRIGRS modeling result—800 mm for 24 h.</p> Full article ">
25 pages, 4886 KiB  
Article
The Geography of Taste: Using Yelp to Study Urban Culture
by Sohrab Rahimi, Sam Mottahedi and Xi Liu
ISPRS Int. J. Geo-Inf. 2018, 7(9), 376; https://doi.org/10.3390/ijgi7090376 - 13 Sep 2018
Cited by 14 | Viewed by 7417
Abstract
This study aims to put forth a new method to study the sociospatial boundaries by using georeferenced community-authored reviews for restaurants. In this study, we show that food choice, drink choice, and restaurant ambience can be good indicators of socioeconomic status of the [...] Read more.
This study aims to put forth a new method to study the sociospatial boundaries by using georeferenced community-authored reviews for restaurants. In this study, we show that food choice, drink choice, and restaurant ambience can be good indicators of socioeconomic status of the ambient population in different neighborhoods. To this end, we use Yelp user reviews to distinguish different neighborhoods in terms of their food purchases and identify resultant boundaries in 10 North American metropolitan areas. This dataset includes restaurant reviews as well as a limited number of user check-ins and rating in those cities. We use Natural Language Processing (NLP) techniques to select a set of potential features pertaining to food, drink and ambience from Yelp user comments for each geolocated restaurant. We then select those features which determine one’s choice of restaurant and the rating that he/she provides for that restaurant. After identifying these features, we identify neighborhoods where similar taste is practiced. We show that neighborhoods identified through our method show statistically significant differences based on demographic factors such as income, racial composition, and education. We suggest that this method helps urban planners to understand the social dynamics of contemporary cities in absence of information on service-oriented cultural characteristics of urban communities. Full article
(This article belongs to the Special Issue Volunteered Geographic Information: Emerging Applications in Public Science and Citizen Participation)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Bourdieu’s theory of distinction. Fields refer to different sub-spaces of society such as family groups and work groups. Individuals’ role in these fields is influenced by her symbolic capital.</p> Full article ">Figure 2
<p>Research workflow.</p> Full article ">Figure 3
<p>10-fold cross-validation results for rating predictions.</p> Full article ">Figure 4
<p>Eigen-gaps for different number of clusters and different matrices.</p> Full article ">Figure 5
<p>Silhouette scores for different Ks for different cities (large-grained spatial bins).</p> Full article ">Figure 6
<p>Silhouette scores for different Ks for different cities (small-grained spatial bins).</p> Full article ">Figure 7
<p>F<sub>1</sub> scores resulting from classification for different cities.</p> Full article ">Figure 8
<p>Clustering results overlaid on per capita income map for four cities. As we can see the two clusters clearly correspond with block-group level income per capita map from Census.</p> Full article ">Figure 9
<p>A comparison between a set of features for the two clusters.</p> Full article ">Figure 10
<p>Clustering results with 5 clusters for Boston.</p> Full article ">Figure 11
<p>Cultural interactions between different cities. Similar colors across cities indicate similar tastes.</p> Full article ">
15 pages, 5851 KiB  
Article
Geospatial Assessment of the Post-Earthquake Hazard of the 2017 Pohang Earthquake Considering Seismic Site Effects
by Han-Saem Kim, Chang-Guk Sun and Hyung-Ik Cho
ISPRS Int. J. Geo-Inf. 2018, 7(9), 375; https://doi.org/10.3390/ijgi7090375 - 10 Sep 2018
Cited by 28 | Viewed by 5503 | Correction
Abstract
The 2017 Pohang earthquake (moment magnitude scale: 5.4) was South Korea’s second strongest earthquake in decades, and caused the maximum amount of damage in terms of infrastructure and human injuries. As the epicenters were located in regions with Quaternary sediments, which involve distributions [...] Read more.
The 2017 Pohang earthquake (moment magnitude scale: 5.4) was South Korea’s second strongest earthquake in decades, and caused the maximum amount of damage in terms of infrastructure and human injuries. As the epicenters were located in regions with Quaternary sediments, which involve distributions of thick fill and alluvial geo-layers, the induced damages were more severe owing to seismic amplification and liquefaction. Thus, to identify the influence of site-specific seismic effects, a post-earthquake survey framework for rapid earthquake damage estimation, correlated with seismic site effects, was proposed and applied in the region of the Pohang earthquake epicenter. Seismic zones were determined on the basis of ground motion by classifying sites using the multivariate site classification system. Low-rise structures with slight and moderate earthquake damage were noted to be concentrated in softer sites owing to the low focal depth of the site, topographical effects, and high frequency range of the mainshocks. Full article
(This article belongs to the Special Issue Natural Hazards and Geospatial Information)
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<p>Epicenters of major events of the 2017 Pohang earthquake [<a href="#B1-ijgi-07-00375" class="html-bibr">1</a>].</p> Full article ">Figure 2
<p>Post-earthquake survey framework for rapid earthquake damage estimation correlated with seismic site effects.</p> Full article ">Figure 3
<p>Extended and target areas for spatial modeling of geo-data in Pohang.</p> Full article ">Figure 4
<p>Geospatial grid information of the representative surface datasets: (<b>a</b>) digital elevation model; (<b>b</b>) slope; (<b>c</b>) geological map.</p> Full article ">Figure 5
<p>Geospatial grid information of representative geo-layers: (<b>a</b>) fill layer; (<b>b</b>) alluvial soil; (<b>c</b>) weathered soil; (<b>d</b>) weathered rock.</p> Full article ">Figure 6
<p>Seismic zonation for geotechnical response-parameter-based, site-specific effects: (<b>a</b>) <span class="html-italic">H</span>; (<b>b</b>) <span class="html-italic">V<sub>S</sub></span><sub>30</sub>; (<b>c</b>) <span class="html-italic">T<sub>G</sub></span>.</p> Full article ">Figure 6 Cont.
<p>Seismic zonation for geotechnical response-parameter-based, site-specific effects: (<b>a</b>) <span class="html-italic">H</span>; (<b>b</b>) <span class="html-italic">V<sub>S</sub></span><sub>30</sub>; (<b>c</b>) <span class="html-italic">T<sub>G</sub></span>.</p> Full article ">Figure 7
<p>Seismic zonation for geo-proxy-based, site-specific effects: (<b>a</b>) elevation; (<b>b</b>) slope; (<b>c</b>) geology (geological description).</p> Full article ">Figure 8
<p>Spatial distribution of the earthquake damage categories for majorly damaged buildings and images of buildings.</p> Full article ">Figure 9
<p>Spatial comparison between <span class="html-italic">T<sub>G</sub></span>-based seismic zonation and earthquake damage category of buildings.</p> Full article ">Figure 10
<p>Correlations based on seismic site class: (<b>a</b>) grade of earthquake exposure and number of damaged buildings; (<b>b</b>) building stories.</p> Full article ">
11 pages, 4172 KiB  
Article
Influences of the Shadow Inventory on a Landslide Susceptibility Model
by Cheng-Chien Liu, Wei Luo, Hsiao-Wei Chung, Hsiao-Yuan Yin and Ke-Wei Yan
ISPRS Int. J. Geo-Inf. 2018, 7(9), 374; https://doi.org/10.3390/ijgi7090374 - 9 Sep 2018
Cited by 4 | Viewed by 3894
Abstract
A landslide inventory serves as the basis for assessing landslide susceptibility, hazard, and risk. It is generally prepared from optical imagery acquired from spaceborne or airborne platforms, in which shadows are inevitably found in mountainous areas. The influences of shadow inventory on a [...] Read more.
A landslide inventory serves as the basis for assessing landslide susceptibility, hazard, and risk. It is generally prepared from optical imagery acquired from spaceborne or airborne platforms, in which shadows are inevitably found in mountainous areas. The influences of shadow inventory on a landslide susceptibility model (LSM), however, have not been investigated systematically. This paper employs both the shadow and landslide inventories prepared from eleven Formosat-2 annual images from the I-Lan area in Taiwan acquired from 2005 to 2016, using a semiautomatic expert system. A standard LSM based on the geometric mean of multivariables was used to evaluate the possible errors incurred by neglecting the shadow inventory. The results show that the LSM performance was significantly improved by 49.21% for the top 1% of the most highly susceptible area and that the performance decreased gradually by 15.25% for the top 10% most highly susceptible areas and 9.71% for the top 20% most highly susceptible areas. Excluding the shadow inventory from the calculation of landslide susceptibility index reveals the real contribution of each factor. They are crucial in optimizing the coefficients of a nondeterministic geometric mean LSM, as well as in deriving the threshold of a landslide hazard early warning system. Full article
(This article belongs to the Special Issue Geospatial Methods and Tools for Natural Risk Management and Communications)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>a</b>) A standard false color image of I-Lan taken by Formosat-2 on 24 August 2009. Both the landslide inventory and shadow inventory were prepared using a semiautomatic expert system [<a href="#B24-ijgi-07-00374" class="html-bibr">24</a>] and masked as yellow and white polylines, respectively. (<b>b</b>) The union of all landslide inventories (yellow polygons) and shadow inventories (white polygons) derived from the annual Formosat-2 imagery (2005–2016). The river channel and those regions outside the study area are annotated in blue. The area ratios of landslide inventory and shadow inventory are 3.9% and 34.1%, respectively.</p> Full article ">Figure 2
<p>Bar charts of (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>S</mi> <mi>l</mi> <mi>o</mi> <mi>p</mi> <mi>e</mi> </mrow> </msub> </mrow> </semantics></math> (green bars) vs. <math display="inline"><semantics> <mrow> <mi>A</mi> <msubsup> <mi>R</mi> <mrow> <mi>S</mi> <mi>l</mi> <mi>o</mi> <mi>p</mi> <mi>e</mi> </mrow> <mo>*</mo> </msubsup> </mrow> </semantics></math> (red bars), and (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>W</mi> <mrow> <mi>S</mi> <mi>l</mi> <mi>o</mi> <mi>p</mi> <mi>e</mi> </mrow> </msub> </mrow> </semantics></math> (green bars) vs. <math display="inline"><semantics> <mrow> <msubsup> <mi>W</mi> <mrow> <mi>S</mi> <mi>l</mi> <mi>o</mi> <mi>p</mi> <mi>e</mi> </mrow> <mo>*</mo> </msubsup> </mrow> </semantics></math> (red bars).</p> Full article ">Figure 3
<p>Bar charts of (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>A</mi> <mi>s</mi> <mi>p</mi> <mi>e</mi> <mi>c</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math> (green bars) vs. <math display="inline"><semantics> <mrow> <mi>A</mi> <msubsup> <mi>R</mi> <mrow> <mi>A</mi> <mi>s</mi> <mi>p</mi> <mi>e</mi> <mi>c</mi> <mi>t</mi> </mrow> <mo>*</mo> </msubsup> </mrow> </semantics></math> (red bars), and (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>W</mi> <mrow> <mi>A</mi> <mi>s</mi> <mi>p</mi> <mi>e</mi> <mi>c</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math> (green bars) vs. <math display="inline"><semantics> <mrow> <msubsup> <mi>W</mi> <mrow> <mi>A</mi> <mi>s</mi> <mi>p</mi> <mi>e</mi> <mi>c</mi> <mi>t</mi> </mrow> <mo>*</mo> </msubsup> </mrow> </semantics></math> (red bars).</p> Full article ">Figure 4
<p>Bar charts of (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>T</mi> <mi>F</mi> </mrow> </msub> </mrow> </semantics></math> (green bars) vs. <math display="inline"><semantics> <mrow> <mi>A</mi> <msubsup> <mi>R</mi> <mrow> <mi>T</mi> <mi>F</mi> </mrow> <mo>*</mo> </msubsup> </mrow> </semantics></math> (red bars), and (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>W</mi> <mrow> <mi>T</mi> <mi>F</mi> </mrow> </msub> </mrow> </semantics></math> (green bars) vs. <math display="inline"><semantics> <mrow> <msubsup> <mi>W</mi> <mrow> <mi>T</mi> <mi>F</mi> </mrow> <mo>*</mo> </msubsup> </mrow> </semantics></math> (red bars).</p> Full article ">Figure 5
<p>Bar charts of (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>A</mi> <msub> <mi>R</mi> <mrow> <mi>L</mi> <mi>i</mi> <mi>t</mi> <mi>h</mi> <mi>o</mi> <mi>l</mi> <mi>o</mi> <mi>g</mi> <mi>y</mi> </mrow> </msub> </mrow> </semantics></math> (green bars) vs. <math display="inline"><semantics> <mrow> <mi>A</mi> <msubsup> <mi>R</mi> <mrow> <mi>L</mi> <mi>i</mi> <mi>t</mi> <mi>h</mi> <mi>o</mi> <mi>l</mi> <mi>o</mi> <mi>g</mi> <mi>y</mi> </mrow> <mo>*</mo> </msubsup> </mrow> </semantics></math> (red bars), and (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>W</mi> <mrow> <mi>L</mi> <mi>i</mi> <mi>t</mi> <mi>h</mi> <mi>o</mi> <mi>l</mi> <mi>o</mi> <mi>g</mi> <mi>y</mi> </mrow> </msub> </mrow> </semantics></math> (green bars) vs. <math display="inline"><semantics> <mrow> <msubsup> <mi>W</mi> <mrow> <mi>L</mi> <mi>i</mi> <mi>t</mi> <mi>h</mi> <mi>o</mi> <mi>l</mi> <mi>o</mi> <mi>g</mi> <mi>y</mi> </mrow> <mo>*</mo> </msubsup> </mrow> </semantics></math> (red bars).</p> Full article ">Figure 6
<p>Maps of (<b>a</b>) <span class="html-italic">LSI</span> (without the correction for shadows) and (<b>b</b>) <span class="html-italic">LSI*</span> (with the correction for shadows) for all cells using Equations (5) and (8), respectively.</p> Full article ">Figure 7
<p>Cumulative percentage of landslide occurrence (CPOLO) based on LSM* (solid line) and LSM (broken line). The percentage of improvement <span class="html-italic">ρ</span> is plotted as a dotted line.</p> Full article ">
27 pages, 9075 KiB  
Article
Sino-InSpace: A Digital Simulation Platform for Virtual Space Environments
by Liang Lyu, Qing Xu, Chaozhen Lan, Qunshan Shi, Wanjie Lu, Yang Zhou and Yinghao Zhao
ISPRS Int. J. Geo-Inf. 2018, 7(9), 373; https://doi.org/10.3390/ijgi7090373 - 8 Sep 2018
Cited by 4 | Viewed by 4540
Abstract
The implementation of increased space exploration missions reduces the distance between human beings and outer space. Although it is impossible for everyone to enter the remote outer space, virtual environments could provide computer-based digital spaces that we can observe, participate in, and experience. [...] Read more.
The implementation of increased space exploration missions reduces the distance between human beings and outer space. Although it is impossible for everyone to enter the remote outer space, virtual environments could provide computer-based digital spaces that we can observe, participate in, and experience. In this study, Sino-InSpace, a digital simulation platform, was developed to support the construction of virtual space environments. The input data are divided into two types, the environment element and the entity object, that are then supported by the unified time-space datum. The platform adopted the pyramid model and octree index to preprocess the geographic and space environment data, which ensured the efficiency of data loading and browsing. To describe objects perfectly, they were abstracted and modeled based on four aspects including attributes, ephemeris, geometry, and behavior. Then, the platform performed the organization of a visual scenario based on logical modeling and data modeling; in addition, it ensured smooth and flexible visual scenario displays using efficient data and rendering engines. Multilevel modes (application directly, visualization development, and scientific analysis) were designed to support multilevel applications for users from different grades and fields. Each mode provided representative case studies, which also demonstrated the capabilities of the platform for data integration, visualization, process deduction, and auxiliary analysis. Finally, a user study with human participants was conducted from multiple views (usability, user acceptance, presence, and software design). The results indicate that Sino-InSpace performs well in simulation for virtual space environments, while a virtual reality setup is beneficial for promoting the experience. Full article
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<p>Platform orientation.</p> Full article ">Figure 2
<p>Space datum of the Sino-InSpace platform.</p> Full article ">Figure 3
<p>Geographic data segmentation and index method.</p> Full article ">Figure 4
<p>Space environment octree subdivision method.</p> Full article ">Figure 5
<p>Description for a BeiDou navigation satellite. Attribute information does not generally change. Structure information is partially related to time because sometimes the components need to adjust. Position and attitude information is always related to time.</p> Full article ">Figure 6
<p>Entity object model design. The action object derives from the component or geometric model itself.</p> Full article ">Figure 7
<p>Scenario organization and modeling based on Unified Modeling Language (UML) and eXtensible Markup Language (XML). (<b>a</b>) UML diagram of the visualization scenario. (<b>b</b>) XML data of the visualization scenario. Timeline is used as an example; its logical model includes the start time, end time, and time step. The datatype of the start time and the end time is defined as dateTime, while that of the time step is Double. XML content was filled according to the field data type, which can be stored in the local file to be accessed later. Furthermore, Timeline was also the part of the Ephemeris and Action classes with the corresponding relationship of 1 to 1.</p> Full article ">Figure 8
<p>Visualization engine design.</p> Full article ">Figure 9
<p>A simple example of the script file.</p> Full article ">Figure 10
<p>Design of platform application modes.</p> Full article ">Figure 11
<p>The operation window of Sino-InSpace in the application directly mode. (<b>a</b>) The main window includes the Scenario Editor, 2D and 3D view, Time Control, and Viewpoint Control; (<b>b</b>) Trajectory setting window; (<b>c</b>) Geometric model setting window; and (<b>d</b>) Action design window.</p> Full article ">Figure 12
<p>Integration and visualization of the constellation and environment data. (<b>a</b>) Stars (Tycho-2 catalogue, number: &gt;2.5 million) and planets in the solar system (NASA Jet Propulsion Laboratory Development Ephemeris (JPL DE) 405); (<b>b</b>) all named features for the moon (number: 8990) [<a href="#B65-ijgi-07-00373" class="html-bibr">65</a>]; (<b>c</b>) geomagnetic field generated from the Tsyganenko 96 model [<a href="#B66-ijgi-07-00373" class="html-bibr">66</a>], which includes the solar-wind-controlled magnetopause, region 1 and 2 Birkeland currents, and the interconnection of the magnetospheric and solar wind fields at the boundary; and (<b>d</b>) geographical data from the southeast area of China (image resolution: 30 m, DEM resolution: 90 m, data amount: &gt;200 GB).</p> Full article ">Figure 13
<p>Launching mission deduction of the Shenzhou spacecraft. (<b>a</b>) Rocket firing. The illustrations introduced the stage name and the capacity of the CZ rocket. (<b>b</b>) Rocket booster separation. The separation action executed 114 s later; (<b>c</b>,<b>d</b>) the process of solar panel expansion and orientation. The solar panel as a component of the spacecraft model was translated and rotated according to the action sequence setting.</p> Full article ">Figure 14
<p>Blinking stereoscopic observations with the help of Nvidia 3D vision technology. The synchronous signal transmitters control the switch of two lenses of 3D glasses, while the 3D view window provides a left or right frame.</p> Full article ">Figure 15
<p>Core component diagram of Sino-InSpace. Arrows indicate dependencies.</p> Full article ">Figure 16
<p>Comprehensive situation system based on Sino-InSpace. Qt Meta-Object Language (QML) was used for the interactive operation and information display.</p> Full article ">Figure 17
<p>Van Allen radiation belt provided by the AE8/AP8 model.</p> Full article ">Figure 18
<p>Space objects collision warning analysis and report output. The distance curves are drawn with ECharts [<a href="#B72-ijgi-07-00373" class="html-bibr">72</a>], a powerful, interactive charting and visualization library for browsers.</p> Full article ">Figure 19
<p>Terrain and geomorphology analysis. The crater closest to Ceti Chasma. The base map was derived from Mars Colorized Viking Mosaic [<a href="#B75-ijgi-07-00373" class="html-bibr">75</a>] and the resolution is 232 m. The source of DEM is MOLA MEGDRs [<a href="#B76-ijgi-07-00373" class="html-bibr">76</a>] and the resolution is 463 m. The amount of segmented geographical data exceeds 15 GB.</p> Full article ">Figure 20
<p>Usability, user acceptance, and presence mean values and standard deviations in different conditions.</p> Full article ">
25 pages, 9531 KiB  
Article
Digital Image Correlation (DIC) Analysis of the 3 December 2013 Montescaglioso Landslide (Basilicata, Southern Italy): Results from a Multi-Dataset Investigation
by Paolo Caporossi, Paolo Mazzanti and Francesca Bozzano
ISPRS Int. J. Geo-Inf. 2018, 7(9), 372; https://doi.org/10.3390/ijgi7090372 - 8 Sep 2018
Cited by 42 | Viewed by 7623
Abstract
Image correlation remote sensing monitoring techniques are becoming key tools for providing effective qualitative and quantitative information suitable for natural hazard assessments, specifically for landslide investigation and monitoring. In recent years, these techniques have been successfully integrated and shown to be complementary and [...] Read more.
Image correlation remote sensing monitoring techniques are becoming key tools for providing effective qualitative and quantitative information suitable for natural hazard assessments, specifically for landslide investigation and monitoring. In recent years, these techniques have been successfully integrated and shown to be complementary and competitive with more standard remote sensing techniques, such as satellite or terrestrial Synthetic Aperture Radar interferometry. The objective of this article is to apply the proposed in-depth calibration and validation analysis, referred to as the Digital Image Correlation technique, to measure landslide displacement. The availability of a multi-dataset for the 3 December 2013 Montescaglioso landslide, characterized by different types of imagery, such as LANDSAT 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor), high-resolution airborne optical orthophotos, Digital Terrain Models and COSMO-SkyMed Synthetic Aperture Radar, allows for the retrieval of the actual landslide displacement field at values ranging from a few meters (2–3 m in the north-eastern sector of the landslide) to 20–21 m (local peaks on the central body of the landslide). Furthermore, comprehensive sensitivity analyses and statistics-based processing approaches are used to identify the role of the background noise that affects the whole dataset. This noise has a directly proportional relationship to the different geometric and temporal resolutions of the processed imagery. Moreover, the accuracy of the environmental-instrumental background noise evaluation allowed the actual displacement measurements to be correctly calibrated and validated, thereby leading to a better definition of the threshold values of the maximum Digital Image Correlation sub-pixel accuracy and reliability (ranging from 1/10 to 8/10 pixel) for each processed dataset. Full article
(This article belongs to the Special Issue Advances in Satellite-Based Mapping and Monitoring of Natural Disasters)
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<p>Schematic diagram of the basic principle of DIC.</p> Full article ">Figure 2
<p>Typologies of the subset transformation order [<a href="#B52-ijgi-07-00372" class="html-bibr">52</a>].</p> Full article ">Figure 3
<p>SW slope of Montescaglioso Village, which was affected by the landslide occurrence (3 December 2013). Local evidence of damage to primary infrastructure and private fields (<b>A</b>,<b>B</b>) obtained during the field survey in July 2016, and buildings (<b>C</b>), from Reference [<a href="#B39-ijgi-07-00372" class="html-bibr">39</a>]. Coordinate system: WGS84 UTM33N.</p> Full article ">Figure 4
<p>Complete COSMO-SkyMed (CSK) Synthetic Aperture Radar (SAR) amplitude imagery dataset used in the analyses (in both ascending and descending geometries). The red line represents the landslide failure occurrence. In this diagram, all the available CSK scenes have been reported by highlighting the scenes used for the temporal average filtering process (in yellow and red) and those used directly in the DIC analyses without applying any type of temporal or spatial filter (in blue and green). The number of CSK SAR absolute amplitude images (in both acquisition geometries) is part of the whole CSK dataset, which covers a wider temporal window.</p> Full article ">Figure 5
<p>Pre- and post-failure CSK SAR amplitude datasets (in ascending geometry). The difference between the absolute amplitude (<b>A</b>,<b>B</b>) and the temporal average amplitude imagery (<b>C</b>,<b>D</b>) is clearly visible.</p> Full article ">Figure 6
<p>Regions of interest (ROIs) (orange polygons) used in approach #1 and located in the immediate surroundings of the landslide (red polygon).</p> Full article ">Figure 7
<p>Regions of interest (ROIs) used in approach #2. Here, the ROIs have been intentionally designed over a wider sector by arbitrarily choosing those areas not affected by the general movement of the slope failure. In (<b>A</b>,<b>B</b>), two different series of ROIs have been highlighted in green tones (<b>A</b>) and red tones (<b>B</b>).</p> Full article ">Figure 8
<p>DIC displacement maps retrieved from CSK SAR ascending absolute amplitude images. Here, an increase in the areas affected by the decorrelated signal from (A) to (D) (highlighted in yellow polygons) in the surrounding sectors of the Montescaglioso landslide area (red polygons) is clearly visible. The time span between the pre-event (3 December 2013) and post-event images increases from the top left to bottom right: (<b>A</b>) 18 December 2013 (15 days); (<b>B</b>) 3 January 2014 (31 days); (<b>C</b>) 4 February 2014 (63 days); and (<b>D</b>) 8 March 2014 (95 days).</p> Full article ">Figure 9
<p>Temporal resolution effect on the decorrelation signal. A direct relationship was achieved, which is also perceptible from the trend line (dashed black line).</p> Full article ">Figure 10
<p>DIC displacement maps derived from CSK SAR absolute amplitude images (in ascending geometry) and analyzed with COSI-Corr software. The extracted vector field (white arrows) of the landslide area (red polygon) is reported. Grey coloring corresponds to areas with loss of correspondence between the pre- and post-failure images.</p> Full article ">Figure 11
<p>DIC displacement map retrieved from ascending CSK SAR temporal average amplitude images with COSI-Corr (<b>A</b>) and GOM Correlate (<b>B</b>) software. The white arrows show the displacement vector field. The red polygons show the landslide boundary. The stable area surrounding the slope failure area is clearly characterized as having no movement. Grey coloring corresponds to areas with loss of correspondence between the pre- and post-failure images. Therefore, the displacement magnitude effect induced an important or total variation on the morphology.</p> Full article ">Figure 12
<p>DIC displacement map retrieved from descending CSK SAR temporal average amplitude images with COSI-Corr software. The white arrows show the displacement vector field. The red polygons show the landslide boundary. Grey coloring corresponds to areas with loss of correspondence between the pre- and post-failure images.</p> Full article ">Figure 13
<p>DIC displacement map retrieved from LANDSAT 8 OLI-TIRS (Operational Land Imager-Thermal Infrared Sensor) images and analyzed with COSI-Corr software. White arrows show the displacement vector field. The red polygon shows the landslide boundary. Grey coloring corresponds to areas with loss of correspondence between the pre- and post-failure images.</p> Full article ">Figure 14
<p>DIC displacement map retrieved from shaded Digital Terrain Models (DTMs) derived images with COSI-Corr (<b>A</b>) and GOM Correlate (<b>B</b>) software. The white arrows show the displacement vector field. The red polygons show the landslide boundary. Grey coloring corresponds to areas with loss of correspondence between the pre- and post-failure images.</p> Full article ">Figure 15
<p>DIC displacement map retrieved from the HR (high-resolution) optical orthophoto with COSI-Corr software. The white arrows represent the displacement vector field direction of the landslide (red polygon), which is consistent with the other displacement maps. Grey coloring corresponds to areas with loss of correspondence between the pre- and post-failure.</p> Full article ">Figure 16
<p>Comparison between DIC analyses retrieved from the DTM dataset using COSI-Corr and GOM Correlate software.</p> Full article ">Figure 17
<p>Diagram showing the background noise (in pixels) histograms for each dataset considered using COSI-Corr software. Each curve represents the percentage of pixels (on the y-axis) characterized by different values of background noise/displacement accuracy (on the x-axis) for each dataset. For each curve, the most likely value of displacement accuracy (i.e., the one with the highest percentage of pixels) and the reliability interval above 30% of the measured background noise are shown.</p> Full article ">Figure 18
<p>Diagram showing the background noise (in meters) histograms for each dataset considered using COSI-Corr software. Each curve represents the percentage of pixels (on the y-axis) characterized by different values of background noise/displacement accuracy (on the x-axis) for each dataset. For each curve, the most likely value of the displacement accuracy (i.e., the one with the highest percentage of pixels) and the reliability interval above 30% of the measured background noise are shown.</p> Full article ">
18 pages, 6520 KiB  
Article
An Efficient Graph-Based Spatio-Temporal Indexing Method for Task-Oriented Multi-Modal Scene Data Organization
by Bin Feng, Qing Zhu, Mingwei Liu, Yun Li, Junxiao Zhang, Xiao Fu, Yan Zhou, Maosu Li, Huagui He and Weijun Yang
ISPRS Int. J. Geo-Inf. 2018, 7(9), 371; https://doi.org/10.3390/ijgi7090371 - 8 Sep 2018
Cited by 7 | Viewed by 5623
Abstract
Task-oriented scene data in big data and cloud environments of a smart city that must be time-critically processed are dynamic and associated with increasing complexities and heterogeneities. Existing hybrid tree-based external indexing methods are input/output (I/O)-intensive, query schema-fixed, and difficult when representing the [...] Read more.
Task-oriented scene data in big data and cloud environments of a smart city that must be time-critically processed are dynamic and associated with increasing complexities and heterogeneities. Existing hybrid tree-based external indexing methods are input/output (I/O)-intensive, query schema-fixed, and difficult when representing the complex relationships of real-time multi-modal scene data; specifically, queries are limited to a certain spatio-temporal range or a small number of selected attributes. This paper proposes a new spatio-temporal indexing method for task-oriented multi-modal scene data organization. First, a hybrid spatio-temporal index architecture is proposed based on the analysis of the characteristics of scene data and the driving forces behind the scene tasks. Second, a graph-based spatio-temporal relation indexing approach, named the spatio-temporal relation graph (STR-graph), is constructed for this architecture. The global graph-based index, internal and external operation mechanisms, and optimization strategy of the STR-graph index are introduced in detail. Finally, index efficiency comparison experiments are conducted, and the results show that the STR-graph performs excellently in index generation and can efficiently address the diverse requirements of different visualization tasks for data scheduling; specifically, the STR-graph is more efficient when addressing complex and uncertain spatio-temporal relation queries. Full article
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<p>Architecture of the hybrid spatio-temporal index (I/O: Input/Output, AR&amp;VR: Augmented Reality and Virtual Reality).</p> Full article ">Figure 2
<p>Framework of the graph-based index.</p> Full article ">Figure 3
<p>The classification of relations and some typical examples.</p> Full article ">Figure 4
<p>An example of construction of the graph-based index.</p> Full article ">Figure 5
<p>The geostring generation process based on a planar Quad-tree, taking point (104.07, 30.54) as an example.</p> Full article ">Figure 6
<p>Framework and scene data flow of the spatio-temporal relation graph (STR-graph) index.</p> Full article ">Figure 7
<p>The fragments of user data, trajectory data, and social network data.</p> Full article ">Figure 8
<p>Index processing speed curve of the STR-graph and UQE-Index under different datasets.</p> Full article ">Figure 9
<p>Pattern matching subgraph of the spatio-temporal query: (<b>a</b>) a given time interval with different spatial ranges; (<b>b</b>) a given spatial range with different time intervals.</p> Full article ">Figure 10
<p>Time consumption results of the spatio-temporal query.</p> Full article ">Figure 11
<p>Pattern matching subgraph (<b>a</b>) and time consumption results (<b>b</b>) of the spatio-temporal relation query.</p> Full article ">Figure 12
<p>The detailed operation process of explorative visualization based on HoloLens, taking community analysis as a case study.</p> Full article ">
20 pages, 900 KiB  
Article
Raising Semantics-Awareness in Geospatial Metadata Management
by Cristiano Fugazza, Monica Pepe, Alessandro Oggioni, Paolo Tagliolato and Paola Carrara
ISPRS Int. J. Geo-Inf. 2018, 7(9), 370; https://doi.org/10.3390/ijgi7090370 - 7 Sep 2018
Cited by 8 | Viewed by 3597
Abstract
Geospatial metadata are often encoded in formats that either are not aimed at efficient retrieval of resources or are plainly outdated. Particularly, the quantum leap represented by the Semantic Web did not induce so far a consistent, interlinked baseline in the geospatial domain. [...] Read more.
Geospatial metadata are often encoded in formats that either are not aimed at efficient retrieval of resources or are plainly outdated. Particularly, the quantum leap represented by the Semantic Web did not induce so far a consistent, interlinked baseline in the geospatial domain. Datasets, scientific literature related to them, and ultimately the researchers behind these products are only loosely connected; the corresponding metadata intelligible only to humans, duplicated in different systems, seldom consistently. We address these issues by relating metadata items to resources that represent keywords, institutes, researchers, toponyms, and virtually any RDF data structure made available over the Web via SPARQL endpoints. Essentially, our methodology fosters delegated metadata management as the entities referred to in metadata are independent, decentralized data structures with their own life cycle. Our example implementation of delegated metadata envisages: (i) editing via customizable web-based forms (including injection of semantic information); (ii) encoding of records in any XML metadata schema; and (iii) translation into RDF. Among the semantics-aware features that this practice enables, we present a worked-out example focusing on automatic update of metadata descriptions. Our approach, demonstrated in the context of INSPIRE metadata (the ISO 19115/19119 profile eliciting integration of European geospatial resources) is also applicable to a broad range of metadata standards, including non-geospatial ones. Full article
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<p>Nodes in ISO-compliant metadata indicating “John Doe” as both creator and custodian of resource “Dataset ABC’.</p> Full article ">Figure 2
<p>Assisted editing of the “Responsible party” section of INSPIRE metadata.</p> Full article ">Figure 3
<p>Workflow addressing: (<b>a</b>) production of the XML and RDF representations of the metadata record; and (<b>b</b>) on-demand generation of the XML representation.</p> Full article ">
21 pages, 3374 KiB  
Article
GIS-Assisted Prediction and Risk Zonation of Wildlife Attacks in the Chitwan National Park in Nepal
by Aleš Ruda, Jaromír Kolejka and Thakur Silwal
ISPRS Int. J. Geo-Inf. 2018, 7(9), 369; https://doi.org/10.3390/ijgi7090369 - 7 Sep 2018
Cited by 21 | Viewed by 7827
Abstract
Population growth forces the human community to expand into the natural habitats of wild animals. Their efforts to use natural sources often collide with wildlife attacks. These animals do not only protect their natural environment, but in the face of losing the potential [...] Read more.
Population growth forces the human community to expand into the natural habitats of wild animals. Their efforts to use natural sources often collide with wildlife attacks. These animals do not only protect their natural environment, but in the face of losing the potential food sources, they also penetrate in human settlements. The research was situated in the Chitwan National Park (CNP) in Nepal, and the aim of this study was to investigate possible geospatial connections between attacks of all kinds of animals on humans in the CNP and its surroundings between 2003 and 2013. The patterns of attacks were significantly uneven across the months, and 89% of attacks occurred outside the park. In total, 74% attacks occurred in the buffer zone forests and croplands within 1 km from the park. There was a strong positive correlation among the number of victims for all attacking animals with a maximum of one victim per 4 km2, except elephant and wild boar. The density of bear victims was higher where the tiger and rhino victims were lower, e.g., in the Madi valley. The data collected during this period did not show any signs of spatial autocorrelation. The calculated magnitude per unit area using the kernel density, together with purpose-defined land use groups, were used to determine five risk zones of wildlife attacks. In conclusion, it was found that the riskiest areas were locations near the forest that were covered by agricultural land and inhabited by humans. Our research results can support any local spatial decision-making processes for improving the co-existence of natural protection in the park and the safety of human communities living in its vicinity. Full article
(This article belongs to the Special Issue GIS for Safety & Security Management)
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<p>Nepal: The location of Chitwan National Park and the buffer zone, with coverage of the physiographic zones (Legend: 1. High Himalaya, 2. High Mountain, 3. Mid-Mountain, 4. Siwalik, 5. Terai).</p> Full article ">Figure 2
<p>Location of incident sites in and around Chitwan National Park during 2003–2013.</p> Full article ">Figure 3
<p>Methodological flowchart of the research.</p> Full article ">Figure 4
<p>Environment types of Chitwan National Park (NP) and the buffer zones.</p> Full article ">Figure 5
<p>Temporal patterns of wildlife attacks on people by (<b>a</b>) season, (<b>b</b>) month, and (<b>c</b>) time.</p> Full article ">Figure 6
<p>Land use of Chitwan National Park and buffer zone overlaid with the operational areas of all recorded attacking animals with different territorial densities of the victims (Note: Diagrams (strips) all1, all2, and all3 illustrate the total land use (presenting land use forms making one or more% share in outlined victim density area) of the three territorial densities of attacks of respective animals. Diagrams (strips) show the land use structure in individual zones, characterized by the different densities of the victims of all attackers).</p> Full article ">Figure 7
<p>Generalized models of the relationships between positions of attack sites and two main land use forms (forested and cultivated land) in areas derived by GIS tools using attack sites as attacking animal action ranges. Numbers (1,2,3) depict three small strips on the right side of the models showing the results of the generalizing process running as a consequence of the delineation of animals’ action ranges with decreasing attack density (a–c). Generalization leads to consequent growth of the share of the originally minor land use form.</p> Full article ">Figure 8
<p>Reclassified kernel density results of wildlife attacks in Chitwan NP and the buffer zones.</p> Full article ">Figure 9
<p>Risk analysis of wildlife attacks in Chitwan National Park and buffer zones.</p> Full article ">
15 pages, 277 KiB  
Review
Critical Review of Methods to Estimate PM2.5 Concentrations within Specified Research Region
by Guangyuan Zhang, Xiaoping Rui and Yonglei Fan
ISPRS Int. J. Geo-Inf. 2018, 7(9), 368; https://doi.org/10.3390/ijgi7090368 - 7 Sep 2018
Cited by 43 | Viewed by 6635
Abstract
Obtaining PM2.5 data for the entirety of a research region underlies the study of the relationship between PM2.5 and human spatiotemporal activity. A professional sampler with a filter membrane is used to measure accurate values of PM2.5 at single points [...] Read more.
Obtaining PM2.5 data for the entirety of a research region underlies the study of the relationship between PM2.5 and human spatiotemporal activity. A professional sampler with a filter membrane is used to measure accurate values of PM2.5 at single points in space. However, there are numerous PM2.5 sampling and monitoring facilities that rely on data from only representative points, and which cannot measure the data for the whole region of research interest. This provides the motivation for researching the methods of estimation of particulate matter in areas having fewer monitors at a special scale, an approach now attracting considerable academic interest. The aim of this study is to (1) reclassify and particularize the most frequently used approaches for estimating the PM2.5 concentrations covering an entire research region; (2) list improvements to and integrations of traditional methods and their applications; and (3) compare existing approaches to PM2.5 estimation on the basis of accuracy and applicability. Full article
(This article belongs to the Special Issue Spatial Analysis of Pollution and Risk in a Changing Climate)
20 pages, 41607 KiB  
Article
Single-Tree Detection in High-Resolution Remote-Sensing Images Based on a Cascade Neural Network
by Dong Tianyang, Zhang Jian, Gao Sibin, Shen Ying and Fan Jing
ISPRS Int. J. Geo-Inf. 2018, 7(9), 367; https://doi.org/10.3390/ijgi7090367 - 6 Sep 2018
Cited by 20 | Viewed by 4601
Abstract
Traditional single-tree detection methods usually need to set different thresholds and parameters manually according to different forest conditions. As a solution to the complicated detection process for non-professionals, this paper presents a single-tree detection method for high-resolution remote-sensing images based on a cascade [...] Read more.
Traditional single-tree detection methods usually need to set different thresholds and parameters manually according to different forest conditions. As a solution to the complicated detection process for non-professionals, this paper presents a single-tree detection method for high-resolution remote-sensing images based on a cascade neural network. In this method, we firstly calibrated the tree and non-tree samples in high-resolution remote-sensing images to train a classifier with the backpropagation (BP) neural network. Then, we analyzed the differences in the first-order statistic features, such as energy, entropy, mean, skewness, and kurtosis of the tree and non-tree samples. Finally, we used these features to correct the BP neural network model and build a cascade neural network classifier to detect a single tree. To verify the validity and practicability of the proposed method, six forestlands including two areas of oil palm in Thailand, and four areas of small seedlings, red maples, or longan trees in China were selected as test areas. The results from different methods, such as the region-growing method, template-matching method, BP neural network, and proposed cascade-neural-network method were compared considering these test areas. The experimental results show that the single-tree detection method based on the cascade neural network exhibited the highest root mean square of the matching rate (RMS_Rmat = 90%) and matching score (RMS_M = 68) in all the considered test areas. Full article
(This article belongs to the Special Issue Geographic Information Science in Forestry)
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<p>Six different test areas and the corresponding reference data.</p> Full article ">Figure 1 Cont.
<p>Six different test areas and the corresponding reference data.</p> Full article ">Figure 2
<p>Flowchart for the single-tree detection method.</p> Full article ">Figure 3
<p>Calibration of samples.</p> Full article ">Figure 4
<p>Sample-extension demonstration.</p> Full article ">Figure 5
<p>Structural design of a backpropagation (BP) neural network.</p> Full article ">Figure 6
<p>3–3-layer cascade-neural-network model.</p> Full article ">Figure 7
<p>3–4-layer cascade-neural-network model.</p> Full article ">Figure 8
<p>An example of non-maximal suppression (NMS). The rectangles represent a detected tree, and the green rectangle indicates the biggest classification probability.</p> Full article ">Figure 9
<p>Training results using the BP neural-network model with 150 neurons in the hidden layer.</p> Full article ">Figure 10
<p>Training results using the BP neural-network model with 300 neurons in the hidden layer.</p> Full article ">Figure 11
<p>Training results using the BP neural-network model with 450 neurons in the hidden layer.</p> Full article ">Figure 12
<p>Training results of the 3–3-layer network model.</p> Full article ">Figure 13
<p>Training results of the 3–4-layer network model.</p> Full article ">Figure 14
<p>The first-order statistical features of the samples.</p> Full article ">Figure 15
<p>The detection result of each test area. The red rectangle represents a detected tree.</p> Full article ">Figure 15 Cont.
<p>The detection result of each test area. The red rectangle represents a detected tree.</p> Full article ">
3 pages, 177 KiB  
Editorial
Introduction to the Special Issue: “State-of-the-Art Virtual/Augmented Reality and 3D Modeling Techniques for Virtual Urban Geographic Experiments”
by Jianming Liang, Jianhua Gong and Yu Liu
ISPRS Int. J. Geo-Inf. 2018, 7(9), 366; https://doi.org/10.3390/ijgi7090366 - 5 Sep 2018
Cited by 2 | Viewed by 3558
Abstract
n/a Full article
13 pages, 19288 KiB  
Article
Spatial Assessment of the Potential Impact of Infrastructure Development on Biodiversity Conservation in Lowland Nepal
by Roshan Sharma, Bhagawat Rimal, Nigel Stork, Himlal Baral and Maheshwar Dhakal
ISPRS Int. J. Geo-Inf. 2018, 7(9), 365; https://doi.org/10.3390/ijgi7090365 - 5 Sep 2018
Cited by 15 | Viewed by 6570
Abstract
Biodiversity is declining at an unprecedented rate with infrastructure development being one of the leading causes. New infrastructure, such as roads, provides new access and results in increased land clearing and wildlife hunting. A number of large infrastructure projects, including new roads and [...] Read more.
Biodiversity is declining at an unprecedented rate with infrastructure development being one of the leading causes. New infrastructure, such as roads, provides new access and results in increased land clearing and wildlife hunting. A number of large infrastructure projects, including new roads and rail, are being planned in Nepal. We show the application of readily available remotely sensed data and geospatial tools to assess the potential impact of these future developments on habitat quality under three protection-level scenarios. Our findings reveal that there is currently large spatial heterogeneity in habitat quality across the landscape as a result of current anthropogenic threats, and that three areas in particular could have up to 40% reduction in habitat quality as a result of the planned infrastructure. Further research is required to determine more precisely the impact on key species. Strengthening protected areas and buffer zones will contribute to mitigating degradation to some degree, however, large areas of biologically significant areas outside protected areas will be affected without new controls. Our geographic information systems (GIS) based methodology could be used to conduct studies in data poor developing countries, where rapid infrastructure development across ecological sites are ongoing, in order to make society, policy makers, and development planners aware. Full article
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<p>Map of Terai Arc Landscape.</p> Full article ">Figure 2
<p>Protected areas and current and proposed infrastructure network of the Terai Arc Landscape.</p> Full article ">Figure 3
<p>Land use and land cover (LULC) of the Terai Arc Landscape.</p> Full article ">Figure 4
<p>Spatial distribution of current habitat quality in Terai Arc Landscape.</p> Full article ">Figure 5
<p>Habitat quality (HQ) loss (in percent) for three protection level scenarios (low protection, current protection, and high protection) with insets for three areas that are precited to be most affected by new infrastructure development.</p> Full article ">
25 pages, 1561 KiB  
Review
Revisiting the Role of Place in Geographic Information Science
by Helena Merschdorf and Thomas Blaschke
ISPRS Int. J. Geo-Inf. 2018, 7(9), 364; https://doi.org/10.3390/ijgi7090364 - 5 Sep 2018
Cited by 21 | Viewed by 13981
Abstract
Although place-based investigations into human phenomena have been widely conducted in the social sciences over the last decades, this notion has only recently transgressed into Geographic Information Science (GIScience). Such a place-based GIS comprises research from computational place modeling on one end of [...] Read more.
Although place-based investigations into human phenomena have been widely conducted in the social sciences over the last decades, this notion has only recently transgressed into Geographic Information Science (GIScience). Such a place-based GIS comprises research from computational place modeling on one end of the spectrum, to purely theoretical discussions on the other end. Central to all research that is concerned with place-based GIS is the notion of placing the individual at the center of the investigation, in order to assess human-environment relationships. This requires the formalization of place, which poses a number of challenges. The first challenge is unambiguously defining place, to subsequently be able to translate it into binary code, which computers and geographic information systems can handle. This formalization poses the next challenge, due to the inherent vagueness and subjectivity of human data. The last challenge is ensuring the transferability of results, requiring large samples of subjective data. In this paper, we re-examine the meaning of place in GIScience from a 2018 perspective, determine what is special about place, and how place is handled both in GIScience and in neighboring disciplines. We, therefore, adopt the view that space is a purely geographic notion, reflecting the dimensions of height, depth, and width in which all things occur and move, while place reflects the subjective human perception of segments of space based on context and experience. Our main research questions are whether place is or should be a significant (sub)topic in GIScience, whether it can be adequately addressed and handled with established GIScience methods, and, if not, which other disciplines must be considered to sufficiently account for place-based analyses. Our aim is to conflate findings from a vast and dynamic field in an attempt to position place-based GIS within the broader framework of GIScience. Full article
(This article belongs to the Special Issue Place-Based Research in GIScience and Geoinformatics)
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<p>The notion of place in relation to space and perception.</p> Full article ">Figure 2
<p>Disciplines identified as contributing to place research within GIScience.</p> Full article ">Figure 3
<p>Repositioning the identified branches of place-based GIS in relation to the assessed uniqueness of GIScience.</p> Full article ">
21 pages, 1855 KiB  
Article
Cross-Domain Building Models—A Step towards Interoperability
by Laura Knoth, Johannes Scholz, Josef Strobl, Manfred Mittlböck, Bernhard Vockner, Caroline Atzl, Abbas Rajabifard and Behnam Atazadeh
ISPRS Int. J. Geo-Inf. 2018, 7(9), 363; https://doi.org/10.3390/ijgi7090363 - 4 Sep 2018
Cited by 19 | Viewed by 7007
Abstract
Buildings have a multifunctional character, which makes it hard to define just one model for all their diverse functions. As these diverse functions are addressed by actors of different perspectives and domain backgrounds, the possibility to exchange available building information would be desirable. [...] Read more.
Buildings have a multifunctional character, which makes it hard to define just one model for all their diverse functions. As these diverse functions are addressed by actors of different perspectives and domain backgrounds, the possibility to exchange available building information would be desirable. Two main models for the creation of building information are Industry Foundation Classes/Building Information Modelling (IFC/BIM) and City Geography Markup Language (CityGML). As the importance of information interchange has been recognized, several authors have tried to develop intermediate models for the information exchange between IFC/BIM and CityGML, e.g., the Unified Building Model (UBM), the BIM Oriented Indoor data Model (BO-IDM), the Indoor Emergency Spatial Model (IESM) and the BIM-GIS integration model for Flood Damage Assessment (FDA model). Nevertheless, all these models have been created with a certain use in mind. Our focus in this article is to identify common elements amongst these proposed models and to combine them into one “core model” that is as simple as possible, while simultaneously containing all important elements. Furthermore, this base model extracted from proposed intermediate models can then be expanded to serve specific use requirements, while still being exchangeable. To show this cross-domain character of the core model, we validated the resulting model with two cases of use (production environment/maintenance and 3D digital cadaster). Full article
(This article belongs to the Special Issue Building Information Modeling and 3D GIS Integration: From the Theoretical to the Practical)
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<p>The core model concept with possible extensions.</p> Full article ">Figure 2
<p>Cartography–Architecture loop (adapted after [<a href="#B14-ijgi-07-00363" class="html-bibr">14</a>]).</p> Full article ">Figure 3
<p>Taxonomy of Indoor spatial models (adapted after [<a href="#B20-ijgi-07-00363" class="html-bibr">20</a>]).</p> Full article ">Figure 4
<p>Modelling process of the new core model.</p> Full article ">Figure 5
<p>The resulting core models with its elements and relationships between them.</p> Full article ">Figure 6
<p>The core model extended with Land Administration Domain Model (LADM) concepts for 3D digital cadasters.</p> Full article ">
21 pages, 5712 KiB  
Article
Road Extraction from VHR Remote-Sensing Imagery via Object Segmentation Constrained by Gabor Features
by Li Chen, Qing Zhu, Xiao Xie, Han Hu and Haowei Zeng
ISPRS Int. J. Geo-Inf. 2018, 7(9), 362; https://doi.org/10.3390/ijgi7090362 - 2 Sep 2018
Cited by 24 | Viewed by 4976
Abstract
Automatic road extraction from remote-sensing imagery plays an important role in many applications. However, accurate and efficient extraction from very high-resolution (VHR) images remains difficult because of, for example, increased data size and superfluous details, the spatial and spectral diversity of road targets, [...] Read more.
Automatic road extraction from remote-sensing imagery plays an important role in many applications. However, accurate and efficient extraction from very high-resolution (VHR) images remains difficult because of, for example, increased data size and superfluous details, the spatial and spectral diversity of road targets, disturbances (e.g., vehicles, shadows of trees, and buildings), the necessity of finding weak road edges while avoiding noise, and the fast-acquisition requirement of road information for crisis response. To solve these difficulties, a two-stage method combining edge information and region characteristics is presented. In the first stage, convolutions are executed by applying Gabor wavelets in the best scale to detect Gabor features with location and orientation information. The features are then merged into one response map for connection analysis. In the second stage, highly complete, connected Gabor features are used as edge constraints to facilitate stable object segmentation and limit region growing. Finally, segmented objects are evaluated by some fundamental shape features to eliminate nonroad objects. The results indicate the validity and superiority of the proposed method to efficiently extract accurate road targets from VHR remote-sensing images. Full article
(This article belongs to the Special Issue Data Mining and Feature Extraction from Satellite Images and Point Cloud Data)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Flowchart of the proposed method.</p> Full article ">Figure 2
<p>(<b>a</b>–<b>h</b>) <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mfrac> <mrow> <mi>n</mi> <mi>π</mi> </mrow> <mn>8</mn> </mfrac> <mo>,</mo> <mo> </mo> <mi>n</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mo> </mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mn>2</mn> <mo>,</mo> <mo>…</mo> <mo>,</mo> <mo> </mo> <mn>7</mn> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>Extraction of Gabor features of interest. (<b>a</b>) Original map with many disturbances; (<b>b</b>) map of merged Gabor features; (<b>c</b>) merged map of Gabor features of interest in each orientation.</p> Full article ">Figure 4
<p>Edge-detection comparison. (<b>a</b>,<b>d</b>) Original image where edge information is not so conspicuous; (<b>b</b>,<b>e</b>) result by canny algorithm, lower- and upper-threshold parameters were set to 50 and 100, respectively; (<b>c</b>,<b>f</b>) erged map by eight orientation filter results (<math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>φ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mn>1.7</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> <mn>0.3</mn> </mrow> </semantics></math> ).</p> Full article ">Figure 5
<p>Sketch map for line segment pair connection.</p> Full article ">Figure 6
<p>Edge constraints. (<b>a</b>) Original map where target road mixes with houses; (<b>b</b>) Gabor features by line-segment detection (LSD) analysis; (<b>c</b>) connection analysis; (<b>d</b>) segmentation with edge constraints.</p> Full article ">Figure 7
<p>Neighbor pixels within Δ<span class="html-italic">D</span>.</p> Full article ">Figure 8
<p>(<b>a</b>) Original map; (<b>b</b>) segmentation results of the method by Gaetano et al. [<a href="#B22-ijgi-07-00362" class="html-bibr">22</a>]; (<b>c</b>) segmentation results of our proposed method.</p> Full article ">Figure 9
<p>Center point at the edge of the object.</p> Full article ">Figure 10
<p>(<b>a</b>) Straight-road-object model; (<b>b</b>) curved-road-object model.</p> Full article ">Figure 11
<p>(<b>a</b>) Original image; (<b>b</b>) preprocessed grayscale map; (<b>c</b>) merged Gabor features in best scale; (<b>d</b>) connected Gabor features of interest; (<b>e</b>) object segmentation with edge constraints; (<b>f</b>) region-growing result; (<b>g</b>) road-object tracking by shape features; (<b>h</b>) skeleton extraction.</p> Full article ">Figure 12
<p>Comparison of the method of Lei et al. [<a href="#B26-ijgi-07-00362" class="html-bibr">26</a>] and the proposed method in an urban area; (<b>a</b>,<b>e</b>) original image; (<b>b</b>,<b>f</b>) road-vector data from manual acquisition; (<b>c</b>,<b>g</b>) results of the method of Lei et al.; (<b>d</b>,<b>h</b>) results of proposed method.</p> Full article ">Figure 13
<p>Comparison of the method of Zang et al. [<a href="#B19-ijgi-07-00362" class="html-bibr">19</a>], Lei et al. [<a href="#B26-ijgi-07-00362" class="html-bibr">26</a>], and the proposed method in a rural area. (<b>a</b>–<b>c</b>) Original image. (<b>d</b>–<b>f</b>) Road-vector data from manual acquisition. (<b>g</b>–<b>i</b>) Results of the method of Zang et al. (<b>j</b>–<b>l</b>) Results of the method of Lei et al. (<b>m</b>–<b>o</b>) Results of proposed method.</p> Full article ">
15 pages, 18838 KiB  
Article
Automatic Seam-Line Detection in UAV Remote Sensing Image Mosaicking by Use of Graph Cuts
by Ming Li, Deren Li, Bingxuan Guo, Lin Li, Teng Wu and Weilong Zhang
ISPRS Int. J. Geo-Inf. 2018, 7(9), 361; https://doi.org/10.3390/ijgi7090361 - 31 Aug 2018
Cited by 8 | Viewed by 5039
Abstract
Image mosaicking is one of the key technologies in data processing in the field of computer vision and digital photogrammetry. For the existing problems of seam, pixel aliasing, and ghosting in mosaic images, this paper proposes and implements an optimal seam-line search method [...] Read more.
Image mosaicking is one of the key technologies in data processing in the field of computer vision and digital photogrammetry. For the existing problems of seam, pixel aliasing, and ghosting in mosaic images, this paper proposes and implements an optimal seam-line search method based on graph cuts for unmanned aerial vehicle (UAV) remote sensing image mosaicking. This paper first uses a mature and accurate image matching method to register the pre-mosaicked UAV images, and then it marks the source of each pixel in the overlapped area of adjacent images and calculates the energy value contributed by the marker by using the target energy function of graph cuts constructed in this paper. Finally, the optimal seam-line can be obtained by solving the minimum value of target energy function based on graph cuts. The experimental results show that our method can realize seamless UAV image mosaicking, and the image mosaic area transitions naturally. Full article
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<p>An UAV image pair and their optical flow field rendering. (<b>a</b>) UAV image pair. (<b>b</b>) Rendering of optical flow field.</p> Full article ">Figure 2
<p>Structure of graph. (<b>a</b>) A directed graph structure. (<b>b</b>) A cut of the graph.</p> Full article ">Figure 3
<p>Images and construct of graph. (<b>a</b>) An analog seam-line in the overlapping area of adjacent images. (<b>b</b>) Diagram of a graph structure.</p> Full article ">Figure 4
<p>The <span class="html-italic">α</span>-<span class="html-italic">β</span> graph.</p> Full article ">Figure 5
<p>Four UAV image pairs. (<b>a</b>) The first pair. (<b>b</b>) The second pair. (<b>c</b>) The third pair. (<b>d</b>) The fourth pair.</p> Full article ">Figure 6
<p>Flow chart of our method.</p> Full article ">Figure 7
<p>Optimal seam-lines of three test methods for <a href="#ijgi-07-00361-f005" class="html-fig">Figure 5</a>a. (<b>a</b>) GCC. (<b>b</b>) GCCG. (<b>c</b>) GCCGOF.</p> Full article ">Figure 8
<p>Optimal seam-lines of three test methods for <a href="#ijgi-07-00361-f005" class="html-fig">Figure 5</a>b. (<b>a</b>) GCC. (<b>b</b>) GCCG. (<b>c</b>) GCCGOF.</p> Full article ">Figure 9
<p>Optimal seam-lines of three test methods for <a href="#ijgi-07-00361-f005" class="html-fig">Figure 5</a>c. (<b>a</b>) GCC. (<b>b</b>) GCCG. (<b>c</b>) GCCGOF.</p> Full article ">Figure 10
<p>Optimal seam-lines of three test methods for <a href="#ijgi-07-00361-f005" class="html-fig">Figure 5</a>d. (<b>a</b>) GCC. (<b>b</b>) GCCG. (<b>c</b>) GCCGOF.</p> Full article ">
16 pages, 6117 KiB  
Article
Share Our Cultural Heritage (SOCH): Worldwide 3D Heritage Reconstruction and Visualization via Web and Mobile GIS
by Hari K. Dhonju, Wen Xiao, Jon P. Mills and Vasilis Sarhosis
ISPRS Int. J. Geo-Inf. 2018, 7(9), 360; https://doi.org/10.3390/ijgi7090360 - 30 Aug 2018
Cited by 29 | Viewed by 9230
Abstract
Despite being of paramount importance to humanity, tangible cultural heritage is often at risk from natural and anthropogenic threats worldwide. As a result, heritage discovery and conservation remain a huge challenge for both developed and developing countries, with heritage sites often inadequately cared [...] Read more.
Despite being of paramount importance to humanity, tangible cultural heritage is often at risk from natural and anthropogenic threats worldwide. As a result, heritage discovery and conservation remain a huge challenge for both developed and developing countries, with heritage sites often inadequately cared for, be it due to a lack of resources, nonrecognition of the value by local people or authorities, human conflict, or some other reason. This paper presents an online geo-crowdsourcing system, termed Share Our Cultural Heritage (SOCH), which can be utilized for large-scale heritage documentation and sharing. Supported by web and mobile GIS, cultural heritage data such as textual stories, locations, and images can be acquired via portable devices. These data are georeferenced and presented to the public via web-mapping. Using photogrammetric modelling, acquired images are used to reconstruct heritage structures or artefacts into 3D digital models, which are then visualized on the SOCH web interface to enable public interaction. This end-to-end system incubates an online virtual community to encourage public engagement, raise awareness, and stimulate cultural heritage ownership. It also provides valuable resources for cultural heritage exploitation, management, education, and monitoring over time. Full article
(This article belongs to the Special Issue Web and Mobile GIS)
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<p>The concept of Share Our Cultural Heritage (SOCH) being a bridge between the public and heritage.</p> Full article ">Figure 2
<p>The conceptual data flow between a user and the backend of the SOCH platform.</p> Full article ">Figure 3
<p>SOCH system architecture.</p> Full article ">Figure 4
<p>The web interface of SOCH.</p> Full article ">Figure 5
<p>Heritage album details and spam-report function.</p> Full article ">Figure 6
<p>Mobile client application.</p> Full article ">Figure 7
<p>Examples of Nepalese heritage structures and 3D models created using SOCH.</p> Full article ">Figure 7 Cont.
<p>Examples of Nepalese heritage structures and 3D models created using SOCH.</p> Full article ">Figure 8
<p>The photo album and 3D model of Newcastle University Arches.</p> Full article ">
19 pages, 5824 KiB  
Article
Capturing Flood Risk Perception via Sketch Maps
by Carolin Klonner, Tomás J. Usón, Sabrina Marx, Franz-Benjamin Mocnik and Bernhard Höfle
ISPRS Int. J. Geo-Inf. 2018, 7(9), 359; https://doi.org/10.3390/ijgi7090359 - 30 Aug 2018
Cited by 18 | Viewed by 6726
Abstract
The fact that an increasing number of people and local authorities are affected by natural hazards, especially floods, highlights the necessity of adequate mitigation and preparedness within disaster management. Many governments, though, have only insufficient monetary or technological capacities. One possible approach to [...] Read more.
The fact that an increasing number of people and local authorities are affected by natural hazards, especially floods, highlights the necessity of adequate mitigation and preparedness within disaster management. Many governments, though, have only insufficient monetary or technological capacities. One possible approach to tackle these issues is the acquisition of information by sketch maps complemented by questionnaires, which allows to digitally capture flood risk perception. We investigate which factors influence information collected by sketch maps and questionnaires in case studies in an area prone to pluvial flooding in Santiago de Chile. Our aim is to gain more information about the methods applied. Hereby, we focus on the spatial acquisition scale of sketch maps and personal characteristics of the participants, for example, whether they live at this very location of the survey (residents) or are pedestrians passing by. Our results show that the choice of the acquisition scale of the base map influences the amount and level of detail of information captured via sketch maps. Thus, detail base maps lead to more precise results when compared to reference data, especially in the case of residents. The results also reveal that the place of living of the respondents has an effect on the resulting information because on the neighborhood level the risk perception of residents is more detailed than the one of pedestrians. The study suggests that the integration of citizens via sketch maps can provide information about flood risk perception, and thus can influence the flood mitigation in the area. Full article
(This article belongs to the Special Issue Volunteered Geographic Information: Emerging Applications in Public Science and Citizen Participation)
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<p>Workflow showing the different tasks and results of each of the steps of the research. After the design of the case study and the conduction of the field work, the preprocessing of the collected data takes place. These data are analyzed in detail and conclusions are drawn.</p> Full article ">Figure 2
<p>Sketch maps with a single participant‘s risk perception marked in red. (<b>a</b>) Overview base layer of La Florida, Santiago de Chile; (<b>b</b>) detailed view of the study area. QR-Code and black dots allow fast automatic georeferencing of the sketch map. Based on OSM Field Papers [<a href="#B31-ijgi-07-00359" class="html-bibr">31</a>].</p> Full article ">Figure 3
<p>Santiago de Chile with its municipalities of Quilicura (first case study in 2015), and La Florida (second case study in 2016 with a study area in the north and the south, respectively).</p> Full article ">Figure 4
<p>Quilicura: The street “Lo Ovalle” turns into a riverbed during rainfalls due to its lower level compared to the side streets. Blocked gullies increase the runoff (photos taken by author, Quilicura, 6 May 2015).</p> Full article ">Figure 5
<p>Quilicura: Risk perception based on the overview base maps. The blue areas summarize the results of the risk perception maps of the 14 participants while the points indicate the reference data based on the local risk perception from 36 participants; i.e., the intensity of their flood risk perception at that direct location. The darker the blue, the higher the intensity. The orange points indicate the location of the 14 participants during the survey with the OSM field papers.</p> Full article ">Figure 6
<p>Comparison of the risk perception of 12 pedestrians (<b>a</b>) and 18 residents (<b>b</b>) based on the overview maps. The orange points indicate the position of the people during the survey.</p> Full article ">Figure 7
<p>Risk perception based on the detail maps in the north of La Florida (blue). Pedestrians (<b>a</b>) tend to overestimate the area at risk in comparison to residents (<b>b</b>). All perceive the same areas at risk as the local government (triangles). The orange points indicate the position of the people during the survey.</p> Full article ">
16 pages, 6140 KiB  
Article
Spatial-Temporal Analysis of Human Dynamics on Urban Land Use Patterns Using Social Media Data by Gender
by Chengcheng Lei, An Zhang, Qingwen Qi, Huimin Su and Jianghao Wang
ISPRS Int. J. Geo-Inf. 2018, 7(9), 358; https://doi.org/10.3390/ijgi7090358 - 29 Aug 2018
Cited by 17 | Viewed by 5407
Abstract
The relationship between urban human dynamics and land use types has always been an important issue in the study of urban problems in China. This paper used location data from Sina Location Microblog (commonly known as Weibo) users to study the human dynamics [...] Read more.
The relationship between urban human dynamics and land use types has always been an important issue in the study of urban problems in China. This paper used location data from Sina Location Microblog (commonly known as Weibo) users to study the human dynamics of the spatial-temporal characteristics of gender differences in Beijing’s Olympic Village in June 2014. We applied mathematical statistics and Local Moran’s I to analyze the spatial-temporal distribution of Sina Microblog users in 100 m × 100 m grids and land use patterns. The female users outnumbered male users, and the sex ratio ( S R varied under different land use types at different times. Female users outnumbered male users regarding residential land and public green land, but male users outnumbered female users regarding workplace, especially on weekends, as the S R on weekends ( S R was 120.5) was greater than that on weekdays ( S R was 118.8). After a Local Moran’s I analysis, we found that High–High grids are primarily distributed across education and scientific research land and residential land; these grids and their surrounding grids have more female users than male users. Low–Low grids are mainly distributed across sports centers and workplaces on weekdays; these grids and their surrounding grids have fewer female users than male users. The average number of users on Saturday was the highest value and, on weekends, the number of female and male users both increased in commercial land, but male users were more active than female users ( S R was 110). Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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<p>Location of the Olympic Village area. (<b>a</b>) Location of the Olympic Village within the administrative divisions of Beijing; (<b>b</b>) remote sensing imagery showing the boundaries of the Olympic Village within central Beijing.</p> Full article ">Figure 2
<p>The locations of Sina Microblog users in June 2014 in and near the Olympic Village, overlaid on 100 m × 100 m grids.</p> Full article ">Figure 3
<p>Variation in the number of Sina Location Microblog users in June 2014: (<b>a</b>) the number of users per day; (<b>b</b>) the histogram of the data.</p> Full article ">Figure 4
<p>Grid transformation of the total number of Sina users in June 2014. Grid IDs are provided for the five grids with the highest numbers of users.</p> Full article ">Figure 5
<p>(<b>a</b>) The local indicator of the spatial association cluster pattern of the difference between <span class="html-italic">SUM<sub>weekday(i)</sub></span> and <span class="html-italic">SUM<sub>weekend(i)</sub></span>; (<b>b</b>) the local indicator of spatial association significance map (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 6
<p>The local indicators of the spatial association cluster pattern of (<b>A</b>) the difference between the number of female users and male users on weekdays and (<b>B</b>) the difference between the number of female users and male users on weekends. (<b>A’</b>,<b>B’</b>) are the local indicators of the spatial association significance map (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 7
<p>Spatial distribution of land used for (<b>A</b>) education and scientific research, (<b>B</b>) commercial uses, (<b>C</b>) public green space, and (<b>D</b>) residences.</p> Full article ">Figure 8
<p>The statistical results of the daily number and sex ratio of users for one week on (<b>a</b>) education and scientific research land, (<b>b</b>) commercial land, (<b>c</b>) public green space land, and (<b>d</b>) residential land.</p> Full article ">
14 pages, 1830 KiB  
Article
Space–Time Analysis of Vehicle Theft Patterns in Shanghai, China
by Yuanyuan Mao, Shenzhi Dai, Jiajun Ding, Wei Zhu, Can Wang and Xinyue Ye
ISPRS Int. J. Geo-Inf. 2018, 7(9), 357; https://doi.org/10.3390/ijgi7090357 - 28 Aug 2018
Cited by 8 | Viewed by 5579
Abstract
To identify and compare the space–time patterns of vehicle thefts and the effects of associated environmental factors, this paper conducts a case study of the Pudong New Area (PNA), a major urban district in Shanghai, China’s largest city. Geographic information system (GIS)-based analysis [...] Read more.
To identify and compare the space–time patterns of vehicle thefts and the effects of associated environmental factors, this paper conducts a case study of the Pudong New Area (PNA), a major urban district in Shanghai, China’s largest city. Geographic information system (GIS)-based analysis indicated that there was a stable pattern of vehicle theft over time. Hotspots of vehicle theft across different time periods were identified. These data provide clues for how law enforcement can prioritize the deployment of limited patrol and investigative resources. Vehicle thefts, especially those of non-motor vehicles, tend to be concentrated in the central-western portion of the PNA, which experienced a dramatic rate of urbanization and has a high concentration of people and vehicles. Important factors contributing to vehicle thefts include a highly mobile and transitory population, a large population density, and high traffic volume. Full article
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<p>Distribution of vehicle theft cases by month (<b>a</b>) and by day (<b>b</b>).</p> Full article ">Figure 2
<p>Non-motor/motor vehicle thefts: standard deviational ellipse and mean centers (<b>a</b>,<b>d</b>), local spatial auto-correlations (<b>b</b>,<b>e</b>), and hierarchical nearest-neighbor clusters (<b>c</b>,<b>f</b>).</p> Full article ">Figure 3
<p>Distribution of vehicle thefts across different types of land use.</p> Full article ">Figure 4
<p>Three types of land use with most non-motor (<b>a</b>) and motor (<b>b</b>) vehicle thefts.</p> Full article ">Figure 4 Cont.
<p>Three types of land use with most non-motor (<b>a</b>) and motor (<b>b</b>) vehicle thefts.</p> Full article ">
17 pages, 7076 KiB  
Article
Studies on Three-Dimensional (3D) Modeling of UAV Oblique Imagery with the Aid of Loop-Shooting
by Jia Li, Yongxiang Yao, Ping Duan, Yun Chen, Shuang Li and Chi Zhang
ISPRS Int. J. Geo-Inf. 2018, 7(9), 356; https://doi.org/10.3390/ijgi7090356 - 27 Aug 2018
Cited by 9 | Viewed by 4958
Abstract
Oblique imagery obtained from an Unmanned Aerial Vehicle (UAV) has been widely applied to large-scale three-dimensional (3D) reconstruction; however, the problems of partially missing model details caused by such factors as occlusion, distortion, and airflow, are still not well resolved. In this paper, [...] Read more.
Oblique imagery obtained from an Unmanned Aerial Vehicle (UAV) has been widely applied to large-scale three-dimensional (3D) reconstruction; however, the problems of partially missing model details caused by such factors as occlusion, distortion, and airflow, are still not well resolved. In this paper, a loop-shooting-aided technology is used to solve the problem of details loss in the 3D model. The use of loop-shooting technology can effectively compensate for losses caused by occlusion, distortion, or airflow during UAV flight and enhance the 3D model details in large scene- modeling applications. Applying this technology involves two key steps. First, based on the 3D modeling construction process, the missing details of the modeling scene are found. Second, using loop-shooting image sets as the data source, incremental iterative fitting based on aerotriangulation theory is used to compensate for the missing details in the 3D model. The experimental data used in this paper were collected from Yunnan Normal University, Chenggong District, Kunming City, Yunnan Province, China. The experiments demonstrate that loop-shooting significantly improves the aerotriangulation accuracy and effectively compensates for defects during 3D large-scale model reconstruction. In standard-scale distance tests, the average relative accuracy of our modeling algorithm reached 99.87% and achieved good results. Therefore, this technique not only optimizes the model accuracy and ensures model integrity, but also simplifies the process of refining the 3D model. This study can be useful as a reference and as scientific guidance in large-scale stereo measurements, cultural heritage protection, and smart city construction. Full article
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<p>(<b>a</b>) is a multirotor unmanned aerial vehicle; (<b>b</b>) is a five-lens camera carried by the UAV.</p> Full article ">Figure 2
<p>Research area map.</p> Full article ">Figure 3
<p>The 3D modeling flowchart.</p> Full article ">Figure 4
<p>Flowchart of incremental 3D modeling aided by loop-shooting.</p> Full article ">Figure 5
<p>Loop-shooting diagram.</p> Full article ">Figure 6
<p>Detailed flowchart of 3D reconstruction aided by loop-shooting.</p> Full article ">Figure 7
<p>(<b>a</b>) The 3D triangulation results for part of the region; (<b>b</b>) Zoomed-in 3D triangulation results.</p> Full article ">Figure 8
<p>(<b>a</b>) Original digital surface model (DSM); (<b>b</b>) Initial 3D model after performing texture mapping.</p> Full article ">Figure 9
<p>(<b>a</b>–<b>d</b>) Representations of the aerotriangulation results aided by varying levels of loop-shooting.</p> Full article ">Figure 10
<p>The 3D model results for the “Fontaine Blanche Hotel:” (<b>a</b>) the overall and regional 3D results for the original model; (<b>b</b>) the overall and regional 3D results aided by one loop-shooting operation; (<b>c</b>) the overall and regional 3D results aided by a second loop-shooting operation; and (<b>d</b>) the overall and regional 3D results aided by a third loop-shooting operation.”.</p> Full article ">Figure 11
<p>Final results: (<b>a</b>) the refined model for the entire research region; (<b>b</b>) the refined model for the side of the “Fontaine Blanche Hotel”; (<b>c</b>) the refined model for the front of the “Fontaine Blanche Hotel”.</p> Full article ">
17 pages, 1255 KiB  
Article
Achieving Complete and Near-Lossless Conversion from IFC to CityGML
by Rudi Stouffs, Helga Tauscher and Filip Biljecki
ISPRS Int. J. Geo-Inf. 2018, 7(9), 355; https://doi.org/10.3390/ijgi7090355 - 27 Aug 2018
Cited by 76 | Viewed by 7630
Abstract
The Singapore Government has embarked on a project to establish a three-dimensional city model and collaborative data platform for Singapore. The research herein contributes to this endeavour by developing a methodology and algorithms to automate the conversion of Building Information Models (BIM), in [...] Read more.
The Singapore Government has embarked on a project to establish a three-dimensional city model and collaborative data platform for Singapore. The research herein contributes to this endeavour by developing a methodology and algorithms to automate the conversion of Building Information Models (BIM), in the Industry Foundation Classes (IFC) data format, into CityGML building models, capturing both geometric and semantic information as available in the BIM models, and including exterior as well as interior structures. We adopt a Triple Graph Grammar (TGG) to formally relate IFC and CityGML, both semantically and geometrically, and to transform a building information model, expressed as an IFC object graph, into a city model expressed as a CityGML object graph. The work pipeline includes extending the CityGML data model with an Application Domain Extension (ADE), which allows capturing information from IFC that is relevant in the geospatial context but at the same time not supported by CityGML in its standard form. In this paper, we elaborate on the triple graph grammar approach and the motivation and roadmap for the development of the ADE. While a fully complete and lossless conversion may never be achieved, this paper suggests that both a TGG and an ADE are natural choices for supporting the conversion between IFC and CityGML. Full article
(This article belongs to the Special Issue Building Information Modeling and 3D GIS Integration: From the Theoretical to the Practical)
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<p>The project in a nutshell: from native BIM (Building Information Model) to the integration of CityGML models in Virtual Singapore.</p> Full article ">Figure 2
<p>A flowchart of the project: from use cases to native BIM requirements and from a native BIM model to a CityGML model and its uses.</p> Full article ">Figure 3
<p>Triple graph consisting of an IFC (Industry Foundation Classes) graph (left, edges with circle end marks), a CityGML graph (right, edges with arrow and marks), and a correlation graph (dashed edges).</p> Full article ">Figure 4
<p>A grammar to create the IFC–CityGML triple graph shown in <a href="#ijgi-07-00355-f003" class="html-fig">Figure 3</a>. For each rule, the left-hand-side (indicated in grey) specifies the correlated IFC and CityGML nodes that must exist before rule application; the right-hand-side of the rule (indicated in black with a plus sign) adds correlated IFC and CityGML nodes and their connections into the existing graph triple.</p> Full article ">Figure 5
<p>IFC-local rule B’ (<b>left</b>) and IFC → CityGML transformation rule B” (<b>right</b>) derived by splitting triple graph grammar rule B from <a href="#ijgi-07-00355-f004" class="html-fig">Figure 4</a>.</p> Full article ">Figure 6
<p>Example of a triple graph rule transforming an interior wall surface from IFC into CityGML. The left-hand-side of the rule (indicated in grey) specifies four IFC nodes, their mutual edges, and a correlated CityGML node; the right-hand-side of the rule (indicated in black with a plus sign) adds a CityGML node and a correlation edge between an existing IFC node and the new CityGML node.</p> Full article ">Figure 7
<p>Example conversion results: Revit advanced tutorial office building (<b>left</b>) and handcrafted two-storey residential building (<b>right</b>). Conversion includes spaces, walls, slabs and roofs, but roofs are omitted for illustrative reasons.</p> Full article ">Figure 8
<p>Excerpt from a simple ADE extending the CityGML data model and supporting the conservation of potentially useful information from architectural models and other sources. Such an enrichment of the data model may benefit the usability of the data in certain applications, e.g., pertaining to the local geographical context.</p> Full article ">
19 pages, 4476 KiB  
Article
On the Risk Assessment of Terrorist Attacks Coupled with Multi-Source Factors
by Xun Zhang, Min Jin, Jingying Fu, Mengmeng Hao, Chongchong Yu and Xiaolan Xie
ISPRS Int. J. Geo-Inf. 2018, 7(9), 354; https://doi.org/10.3390/ijgi7090354 - 27 Aug 2018
Cited by 13 | Viewed by 6102
Abstract
Terrorism has wreaked havoc on today’s society and people. The discovery of the regularity of terrorist attacks is of great significance to the global counterterrorism strategy. In this study, we improve the traditional location recommendation algorithm coupled with multi-source factors and spatial characteristics. [...] Read more.
Terrorism has wreaked havoc on today’s society and people. The discovery of the regularity of terrorist attacks is of great significance to the global counterterrorism strategy. In this study, we improve the traditional location recommendation algorithm coupled with multi-source factors and spatial characteristics. We used the data of terrorist attacks in Southeast Asia from 1970 to 2016, and comprehensively considered 17 influencing factors, including socioeconomic and natural resource factors. The improved recommendation algorithm is used to build a spatial risk assessment model of terrorist attacks, and the effectiveness is tested. The model trained in this study is tested with precision, recall, and F-Measure. The results show that, when the threshold is 0.4, the precision is as high as 88%, and the F-Measure is the highest. We assess the spatial risk of the terrorist attacks in Southeast Asia through experiments. It can be seen that the southernmost part of the Indochina peninsula and the Philippines are high-risk areas and that the medium-risk and high-risk areas are mainly distributed in the coastal areas. Therefore, future anti-terrorism measures should pay more attention to these areas. Full article
(This article belongs to the Special Issue GIS for Safety & Security Management)
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<p>Southeast Asia terrorist attack map.</p> Full article ">Figure 2
<p>Southeast Asia terrorist attack death map.</p> Full article ">Figure 3
<p>Evaluation flow chart.</p> Full article ">Figure 4
<p>Calculation flow chart.</p> Full article ">Figure 5
<p>Clustering quality of four algorithms using different parameters.</p> Full article ">Figure 5 Cont.
<p>Clustering quality of four algorithms using different parameters.</p> Full article ">Figure 6
<p>Comparison of the clustering quality of four algorithms.</p> Full article ">Figure 7
<p>Partition results.</p> Full article ">Figure 8
<p>Kernel density map.</p> Full article ">Figure 9
<p>Verification results.</p> Full article ">Figure 10
<p>Terrorist attack risk map.</p> Full article ">Figure 11
<p>Effect before and after partition.</p> Full article ">Figure 11 Cont.
<p>Effect before and after partition.</p> Full article ">
20 pages, 9417 KiB  
Article
Novel Method for Virtual Restoration of Cultural Relics with Complex Geometric Structure Based on Multiscale Spatial Geometry
by Miaole Hou, Su Yang, Yungang Hu, Yuhua Wu, Lili Jiang, Sizhong Zhao and Putong Wei
ISPRS Int. J. Geo-Inf. 2018, 7(9), 353; https://doi.org/10.3390/ijgi7090353 - 27 Aug 2018
Cited by 23 | Viewed by 5232
Abstract
Because of the age of relics and the lack of historical data, the geometric forms of missing parts can only be judged by the subjective experience of repair personnel, which leads to varying restoration effects when the geometric structure of the complex relic [...] Read more.
Because of the age of relics and the lack of historical data, the geometric forms of missing parts can only be judged by the subjective experience of repair personnel, which leads to varying restoration effects when the geometric structure of the complex relic is reconstructed. Therefore, virtual repair effects cannot fully reflect the historical appearance of cultural relics. In order to solve this problem, this paper presents a virtual restoration method based on the multiscale spatial geometric features of cultural relics in the case of complex construction where the geometric shape of the damaged area is unknown, using the Dazu Thousand-Hand Bodhisattva statue in China as an example. In this study, the global geometric features of the three-dimensional (3D) model are analyzed in space to determine the geometric shape of the damaged parts of cultural relics. The local geometric features are represented by skeleton lines based on regression analysis, and a geometric size prediction model of the defective parts is established, which is used to calculate the geometric dimensions of the missing parts. Finally, 3D surface reconstruction technology is used to quantitate virtual restoration of the defective parts. This method not only provides a new idea for the virtual restoration of artifacts with complex geometric structure, but also may play a vital role in the protection of cultural relics. Full article
(This article belongs to the Special Issue Data Acquisition and Processing in Cultural Heritage)
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<p>Different restoration results due to lack of reliable restoration evidence: (<b>a</b>) damaged finger; (<b>b</b>) possible result 1; (<b>c</b>) possible result 2; and (<b>d</b>) possible result 3.</p> Full article ">Figure 2
<p>Overall technical process of the proposed method.</p> Full article ">Figure 3
<p>Dazu Thousand-Hand Bodhisattva statue: (<b>a</b>) orthophoto map; (<b>b</b>) one damaged hand.</p> Full article ">Figure 4
<p>Equipment used in data acquisition: (<b>a</b>) FARO LS420 scanner; (<b>b</b>) CimCore Infinite 2.0 articulating arm.</p> Full article ">Figure 5
<p>Geometric information of the Dazu Thousand-Hand Bodhisattva statue: (<b>a</b>) orthophoto quad; and (<b>b</b>) dimensions of statue model.</p> Full article ">Figure 6
<p>Spatial partitioning of the Dazu Thousand-Hand Bodhisattva statue: (<b>a</b>) partitioning; (<b>b</b>) line drawing of hand number 9-7-s3.</p> Full article ">Figure 7
<p>Global spatial geometric characteristics analysis of the Dazu Thousand-Hand Bodhisattva statue: (<b>a</b>) example of spatial geometric feature fitting on the object; (<b>b</b>) positions of geometric center point and “sky eye”.</p> Full article ">Figure 7 Cont.
<p>Global spatial geometric characteristics analysis of the Dazu Thousand-Hand Bodhisattva statue: (<b>a</b>) example of spatial geometric feature fitting on the object; (<b>b</b>) positions of geometric center point and “sky eye”.</p> Full article ">Figure 8
<p>Iteration contraction process of cultural relic model.</p> Full article ">Figure 9
<p>Simplification result of QEM (Quadric error metrics) skeleton nodes under different weights: (<b>a</b>) <span class="html-italic">a</span> = 1, and <span class="html-italic">b</span> = 0.1, 130 skeleton nodes; (<b>b</b>) <span class="html-italic">a</span> = 1 and <span class="html-italic">b</span> = 1, 149 skeleton nodes.</p> Full article ">Figure 10
<p>Comparison of skeleton nodes before and after fine-tuning.</p> Full article ">Figure 11
<p>Multiangle displays of skeleton lines.</p> Full article ">Figure 12
<p>Prediction model: (<b>a</b>) hypothesis testing and outlier analysis of the data; (<b>b</b>) prediction model of incomplete finger length.</p> Full article ">Figure 13
<p>Photographs of missing fingers and their reference hands: (<b>a</b>) hand 9-7-s4; (<b>b</b>) hand 9-5-s6; which is the hand symmetric to 9-7-s4.</p> Full article ">Figure 14
<p>Characteristics of symmetry in the Dazu Thousand-Hand Bodhisattva statue: (<b>a</b>) space areas of hands 9-7-s4 (right side) and 9-5-s6 (left side); (<b>b</b>) local enlargement of line drawing with the geometrically similar hand.</p> Full article ">Figure 15
<p>Virtual restoration effect of ID 9-7-s4 hand: (<b>a</b>,<b>b</b>) original model; (<b>c</b>,<b>d</b>) virtual restoration effect.</p> Full article ">
24 pages, 7998 KiB  
Article
Exploring the Factors Driving Changes in Farmland within the Tumen/Tuman River Basin
by Cholhyok Kang, Yili Zhang, Basanta Paudel, Linshan Liu, Zhaofeng Wang and Ryongsu Li
ISPRS Int. J. Geo-Inf. 2018, 7(9), 352; https://doi.org/10.3390/ijgi7090352 - 27 Aug 2018
Cited by 6 | Viewed by 3581
Abstract
Understanding farmland changes and their mechanisms is important for food security and sustainable development. This study assesses the farmland changes and their drivers within the Tumen River of China and the Tuman River within the Democratic People’s Republic of Korea (DPR Korea) from [...] Read more.
Understanding farmland changes and their mechanisms is important for food security and sustainable development. This study assesses the farmland changes and their drivers within the Tumen River of China and the Tuman River within the Democratic People’s Republic of Korea (DPR Korea) from 1991 to 2016 (1991–2000, 2000–2010, and 2010–2016). Farmland surfaces in Tumen/Tuman River Basin (TRB) for each of the years were mapped from satellite imagery using an object-based image segmentation and a support vector machine (SVM) approach. A logistic regression was applied to discern the mechanisms underlying farmland changes. Results indicate that cultivated surfaces changes within the two regions were characterized by large differences during the three time periods. The decreases of cultivated surface of −15.55 km2 (i.e., 0.55% of total cultivated surface area in 2000) and −23.61 km2 (i.e., 0.83% of total cultivated surface area in 2016) occurred in China between 1991 and 2000 and between 2010 and 2016, respectively; while an increase of 30.98 km2 (i.e., 1.09% of total cultivated surface area in 2010) was seen between 2000 and 2010. Cultivated surfaces increased within DPR Korea side over the three time periods; a marked increase, in particular, was seen between 1991 and 2000 by 443.93 km2 (i.e., 23.43% of total cultivated surface area in 2000), while farmland increased by 140.87 km2 (i.e., 6.92% of total cultivated surface area in 2010) and 180.86 km2 (i.e., 1.78% of total cultivated surface area in 2016), respectively, between 2000 and 2010 and between 2010 and 2016. We also found that expansions and contractions in farmland within both regions of the TRB were mainly influenced by topographic, soil, climatic, and distance factors, which had different importance degrees. Among these significant forces, the temperatures in the two regions were paramount positive factors on farmland changes during 1991–2016 and slope in China and precipitation in DPR Korea were the paramount negative factors affecting farmland changes, respectively. Additionally, except for between 2000 and 2010 in DPR Korea TRB region, most of the factors significantly influencing the farmland changes revealed the same positive or negative effects in different periods, because of mountainous topography. This study allows enhancing understanding of the mechanisms underlying farmland changes in the TRB. Full article
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<p>Location of study area and the distribution examples of reference data (<b>a</b>) for 2010 land cover classification and farmland status (<b>b</b>,<b>c</b>).</p> Full article ">Figure 2
<p>Framework overview of this study. Note: SS indicates spatial sampling; ZS is Z-score standardization; MLT is multicollinearity test; LR is logistic regression model.</p> Full article ">Figure 3
<p>Potential 2010 factors considered for the study area.</p> Full article ">Figure 4
<p>The results of land cover (<b>a</b>–<b>d</b>) and farmland classification (<b>e</b>–<b>h</b>) for different years.</p> Full article ">Figure 5
<p>Spatial pattern of farmland expansion across different regions over the three times.</p> Full article ">Figure 6
<p>Spatial pattern of farmland contraction across different regions over the three times.</p> Full article ">Figure 7
<p>Proportions (<b>a</b>,<b>b</b>) of farmland changes across different regions over the three time periods evaluated in this study.</p> Full article ">Figure 8
<p>Comparative analysis of the relative importance of different factors driving the farmland expansions and contractions within Chinese TRB region.</p> Full article ">Figure 9
<p>Comparative analysis of the relative importance of different factors driving the farmland expansions and contractions within the DPR Korea TRB region.</p> Full article ">
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