Abstract
According to the United States National Centers for Environmental Information (NCEI), 2017 was one of the most expensive year of losses due to numerous weather and climate disaster events. To reduce the expenditures handling insurance claims and interactive adjustment of losses, automatic methods analyzing post-disaster images of large areas are increasingly being employed. In our work, roof damage analysis was carried out from high-resolution aerial images captured after a devastating hurricane. We compared the performance of a conventional (Random Forest) classifier, which operates on superpixels and relies on sophisticated, hand-crafted features, with two Convolutional Neural Networks (CNN) for semantic image segmentation, namely, SegNet and DeepLabV3+. The results vary greatly, depending on the complexity of the roof shapes. In case of homogeneous shapes, the results of all three methods are comparable and promising. For complex roof structures the results show that the CNN based approaches perform slightly better than the conventional classifier; the performance of the latter one is, however, most predictable depending on the amount of training data and most successful in the case this amount is low. On the building level, all three classifiers perform comparable well. However, an important prerequisite for accurate damage grading of each roof is its correct delineation. To achieve it, a procedure on multi-modal registration has been developed and summarized in this work. It allows adjusting freely available GIS data with actual image data and it showed a robust performance even in case of severely destroyed buildings.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)
Bodensteiner, C., Hebel, M., Arens, M.: Accurate single image multi-modal camera pose estimation. In: Kutulakos, K.N. (ed.) ECCV 2010. LNCS, vol. 6554, pp. 296–309. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35740-4_23
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Brooks, R., Nelson, T., Amolins, K., Hall, G.B.: Semi-automated building footprint extraction from orthophotos. Geomatica 69(2), 231–244 (2015)
Bulatov, D.: Alignment of building footprints using quasi-nadir aerial photography. In: Felsberg, M., Forssén, P.-E., Sintorn, I.-M., Unger, J. (eds.) SCIA 2019. LNCS, vol. 11482, pp. 361–373. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20205-7_30
Bulatov, D., Häufel, G., Lucks, L., Pohl, M.: Land cover classification in combined elevation and optical images supported by OSM data, mixed-level features, and non-local optimization algorithms. Photogram. Eng. Remote Sens. 85(3), 179–195 (2019)
Bulatov, D., Solbrig, P., Gross, H., Wernerus, P., Repasi, E., Heipke, C.: Context-based urban terrain reconstruction from UAV-videos for geoinformation applications. ISPRS Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. 3822, 75–80 (2011)
Carletta, J.: Assessing agreement on classification tasks: the kappa statistic. Comput. Linguist. 22(2), 249–254 (1996)
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801–818 (2018)
Cooner, A.J., Shao, Y., Campbell, J.B.: Detection of urban damage using remote sensing and machine learning algorithms: revisiting the 2010 Haiti earthquake. Remote Sens. 08–00868(10), 1–17 (2016)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893. IEEE (2005)
Dell’Acqua, F., Gamba, P.: Remote sensing and earthquake damage assessment: experiences, limits, and perspectives. Proc. IEEE 100(10), 2876–2890 (2012)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html
Fujita, A., Sakurada, K., Imaizumi, T., Ito, R., Hikosaka, S., Nakamura, R.: Damage detection from aerial images via convolutional neural networks. In: 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA), pp. 5–8. IEEE (2017)
Gamba, P., Dell’Acqua, F., Odasso, L.: Object-oriented building damage analysis in VHR optical satellite images of the 2004 tsunami over Kalutara, Sri Lanka. In: Urban Remote Sensing Joint Event, 2007, pp. 1–5. IEEE (2007)
Genuer, R., Poggi, J.M., Tuleau-Malot, C.: Variable selection using random forests. Pattern Recogn. Lett. 31(14), 2225–2236 (2010)
Geusebroek, J.M., Van den Boomgaard, R., Smeulders, A.W.M., Geerts, H.: Color invariance. IEEE Trans. Pattern Anal. Mach. Intell. 23(12), 1338–1350 (2001)
Gueguen, L., Hamid, R.: Large-scale damage detection using satellite imagery. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1321–1328 (2015)
Haklay, M.: How good is volunteered geographical information? A comparative study of OpenStreetMap and ordnance survey datasets. Environ. Plann. B Plann. Des. 37(4), 682–703 (2010)
Hermosillo, G., Chefd’Hotel, C., Faugeras, O.: Variational methods for multimodal image matching. Int. J. Comput. Vision 50(3), 329–343 (2002)
Huyck, C.K., Adams, B.J., Cho, S., Chung, H.C., Eguchi, R.T.: Towards rapid citywide damage mapping using neighborhood edge dissimilarities in very high-resolution optical satellite imagery-application to the 2003 Bam, Iran, earthquake. Earthquake Spectra 21(S1), 255–266 (2005)
Im, J., Jensen, J., Tullis, J.: Object-based change detection using correlation image analysis and image segmentation. Int. J. Remote Sens. 29(2), 399–423 (2008)
Lagarias, J.C., Reeds, J.A., Wright, M.H., Wright, P.E.: Convergence properties of the Nelder-Mead Simplex method in low dimensions. SIAM J. Optim. 9(1), 112–147 (1998)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Lucks, L., Pohl, M., Bulatov, D., Thönessen, U.: Superpixel-wise assessment of building damage from aerial images. In: International Conference on Computer Vision Theory and Applications (VISAPP), pp. 211–220 (2019)
Ma, J., Qin, S.: Automatic depicting algorithm of earthquake collapsed buildings with airborne high resolution image. In: 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 939–942. IEEE (2012)
Marcos, D., et al.: Learning deep structured active contours end-to-end. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8877–8885. IEEE (2018)
Peng, J., Zhang, D., Liu, Y.: An improved snake model for building detection from urban aerial images. Pattern Recogn. Lett. 26(5), 587–595 (2005)
Pesaresi, M., Gerhardinger, A., Haag, F.: Rapid damage assessment of built-up structures using VHR satellite data in tsunami-affected areas. Int. J. Remote Sens. 28(13–14), 3013–3036 (2007)
Pohl, M., Meidow, J., Bulatov, D.: Simplification of polygonal chains by enforcing few distinctive edge directions. In: Sharma, P., Bianchi, F.M. (eds.) SCIA 2017. LNCS, vol. 10270, pp. 3–14. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59129-2_1
Rathje, E.M., Woo, K.S., Crawford, M., Neuenschwander, A.: Earthquake damage identification using multi-temporal high-resolution optical satellite imagery. In: Proceedings of the IEEE on Geoscience and Remote Sensing Symposium, vol. 7, pp. 5045–5048. IEEE (2005)
Sirmacek, B., Unsalan, C.: Damaged building detection in aerial images using shadow information. In: 4th International Conference on Recent Advances in Space Technologies, pp. 249–252. IEEE (2009)
Tasar, O., Maggiori, E., Alliez, P., Tarabalka, Y.: Polygonization of binary classification maps using mesh approximation with right angle regularity. In: IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, pp. 6404–6407. IEEE (2018)
Tomowski, D., Klonus, S., Ehlers, M., Michel, U., Reinartz, P.: Change visualization through a texture-based analysis approach for disaster applications. In: ISPRS Proceedings on Advanced Remote Sensing Science, vol. XXXVIII, pp. 263–269 (2010)
Tu, J., Li, D., Feng, W., Han, Q., Sui, H.: Detecting damaged building regions based on semantic scene change from multi-temporal high-resolution remote sensing images. ISPRS Int. J. Geo-Inf. 6(5), 131 (2017)
Vargas-Muñoz, J., Marcos, D., Lobry, S., Dos Santos, J.A., Falcão, A.X., Tuia, D.: Correcting misaligned rural building annotations in open street map using convolutional neural networks evidence. In: International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1284–1287. IEEE (2018)
Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. Int. J. Comput. Vision 62(1–2), 61–81 (2005)
Veksler, O., Boykov, Y., Mehrani, P.: Superpixels and supervoxels in an energy optimization framework. In: Proceeding on European Conference on Computer Vision, pp. 211–224 (2010)
Vetrivel, A., Gerke, M., Kerle, N., Nex, F., Vosselman, G.: Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning. ISPRS J. Photogram. Remote Sens. 140, 45–59 (2017)
Warnke, S., Bulatov, D.: Variable selection for road segmentation in aerial images. ISPRS Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. 42, 297–304 (2017)
Weinmann, M., Jutzi, B., Hinz, S., Mallet, C.: Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers. ISPRS J. Photogram. Remote Sens. 105, 286–304 (2015)
Zampieri, A., Charpiat, G., Girard, N., Tarabalka, Y.: Multimodal image alignment through a multiscale chain of neural networks with application to remote sensing. In: European Conference on Computer Vision (ECCV) (2018)
Zhang, J.F., Xie, L.L., Tao, X.X.: Change detection of remote sensing image for earthquake-damaged buildings and its application in seismic disaster assessment. J. Nat. Disasters 11(2), 59–64 (2002)
Zhu, X.X., et al.: Deep learning in remote sensing: a comprehensive review and list of resources. IEEE Geosci. Remote Sens. Mag. 5(4), 8–36 (2017). https://doi.org/10.1109/MGRS.2017.2762307
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Böge, M., Bulatov, D., Lucks, L. (2020). Localization and Grading of Building Roof Damages in High-Resolution Aerial Images. In: Cláudio, A., et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2019. Communications in Computer and Information Science, vol 1182. Springer, Cham. https://doi.org/10.1007/978-3-030-41590-7_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-41590-7_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-41589-1
Online ISBN: 978-3-030-41590-7
eBook Packages: Computer ScienceComputer Science (R0)