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http://dx.doi.org/10.7848/ksgpc.2022.40.5.381

Quantitative Evaluations of Deep Learning Models for Rapid Building Damage Detection in Disaster Areas  

Ser, Junho (Dept. of Geography, Dongguk University)
Yang, Byungyun (Dept. of Geography Education, Dongguk University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.40, no.5, 2022 , pp. 381-391 More about this Journal
Abstract
This paper is intended to find one of the prevailing deep learning models that are a type of AI (Artificial Intelligence) that helps rapidly detect damaged buildings where disasters occur. The models selected are SSD-512, RetinaNet, and YOLOv3 which are widely used in object detection in recent years. These models are based on one-stage detector networks that are suitable for rapid object detection. These are often used for object detection due to their advantages in structure and high speed but not for damaged building detection in disaster management. In this study, we first trained each of the algorithms on xBD dataset that provides the post-disaster imagery with damage classification labels. Next, the three models are quantitatively evaluated with the mAP(mean Average Precision) and the FPS (Frames Per Second). The mAP of YOLOv3 is recorded at 34.39%, and the FPS reached 46. The mAP of RetinaNet recorded 36.06%, which is 1.67% higher than YOLOv3, but the FPS is one-third of YOLOv3. SSD-512 received significantly lower values than the results of YOLOv3 on two quantitative indicators. In a disaster situation, a rapid and precise investigation of damaged buildings is essential for effective disaster response. Accordingly, it is expected that the results obtained through this study can be effectively used for the rapid response in disaster management.
Keywords
Deep Learning Model; One-Stage Detector; Very High-Resolution Satellite Image; Damaged Detection; Disaster Management;
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1 Perez, L. and Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621. https://doi.org/10.1109/mlbdbi54094.2021.00134   DOI
2 Pesaresi, M., Gerhardinger, A., and Haag, F. (2007). Rapid damage assessment of built-up structures using VHR satellite data in tsunami-affected areas. International Journal of Remote Sensing, Vol. 28, No. 13-14, pp. 3013-3036.   DOI
3 Redmon, J. and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767. https://doi.org/10.48550/arXiv.1804.02767   DOI
4 Scientific Research Working Group for High Resolution Satellite Remote Sensing/RSSJ. (2011). High Resolution Satellite Remote Sensing Concerning the 2011 off the Pacific Coast of Tohoku Earthquake and Tsunami Disaster. Journal of The Remote Sensing Society of Japan, Vol. 31, No. 3, pp. 344-367. https://doi.org/10.11440/rssj.31.344   DOI
5 Van Etten, A., Hogan, D., Manso, J. M., Shermeyer, J., Weir, N., and Lewis, R. (2021). The multi-temporal urban development spacenet dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6398-6407. https://doi.org/10.1109/cvpr46437.2021.00633   DOI
6 Yang, B. (2016). GIS based 3-D landscape visualization for promoting citizen's awareness of coastal hazard scenarios in flood prone tourism towns. Applied Geography, Vol. 76, pp. 85-97. https://doi.org/10.1016/j.apgeog.2016.09.006   DOI
7 Yang, Y., Xie, G., and Qu, Y. (2021). Real-time Detection of Aircraft Objects in Remote Sensing Images Based on Improved YOLOv4. In 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), IEEE, Vol. 5, pp. 1156-1164. https://doi.org/10.1109/iaeac50856.2021.9390673   DOI
8 Zou, Z., Shi, Z., Guo, Y., and Ye, J. (2019). Object detection in 20 years: A survey. arXiv preprint arXiv:1905.05055. https://doi.org/10.48550/arXiv.1905.05055   DOI
9 Bowman, J. and Yang, L. (2021). Few-shot Learning for Postdisaster Structure Damage Assessment. In Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pp. 27-32. https://doi.org/10.1145/3486635.3491071   DOI
10 Deng, L. and Yu, D. (2014). Deep learning: methods and applications. Foundations and trends in signal processing, Vol. 7, No. 3-4, pp. 197-387. https://doi.org/10.1561/9781601988157   DOI
11 Ji, M., Liu, L., and Buchroithner, M. (2018). Identifying collapsed buildings using post-earthquake satellite imagery and convolutional neural networks: A case study of the 2010 Haiti earthquake. Remote Sensing, Vol. 10, No. 11, 1689p. https://doi.org/10.3390/rs10111689   DOI
12 Joyce, K. E., Belliss, S. E., Samsonov, S. V., McNeill, S. J., and Glassey, P. J. (2009). A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters. Progress in physical geography, Vol. 33, No. 2, pp. 183-207. https://doi.org/10.1177/0309133309339563   DOI
13 Kalantar, B., Ueda, N., Al-Najjar, H. A., and Halin, A. A. (2020). Assessment of convolutional neural network architectures for earthquake-induced building damage detection based on pre-and post-event orthophoto images. Remote Sensing, Vol. 12, No. 21, 3529p. https://doi.org/10.3390/rs12213529   DOI
14 Alfarrarjeh, A., Trivedi, D., Kim, S. H., and Shahabi, C. (2018). A deep learning approach for road damage detection from smartphone images. In 2018 IEEE International Conference on Big Data (Big Data), IEEE, pp. 5201-5204. https://doi.org/10.1109/bigdata.2018.8621899   DOI
15 Kim, Y., Lee, S., Kim, J., and Park, Y. (2017). Disaster management using high resolution optical satellite imagery and case analysis. Journal of the Korean Society of Hazard Mitigation, Vol. 17, No. 3, pp. 117-124. https://doi.org/10.9798/KOSHAM.2017.17.3.117   DOI
16 Lin, T. Y., Goyal, P., Girshick, R., He, K., and Dollar, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, pp. 2980-2988. https://doi.org/10.1109/iccv.2017.324   DOI
17 Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., and Berg, A. C. (2016, October). Ssd: Single shot multibox detector. In European conference on computer vision, Springer, Cham, pp. 21-37. https://doi.org/10.1007/978-3-319-46448-0_2   DOI
18 Carranza-Garcia, M., Torres-Mateo, J., Lara-Benitez, P., and Garcia-Gutierrez, J. (2020). On the performance of one-stage and two-stage object detectors in autonomous vehicles using camera data. Remote Sensing, Vol 13, No. 1, 89p. https://doi.org/10.3390/rs13010089   DOI
19 Yang, B. and Jahan, I. (2018). Comprehensive assessment for post-disaster recovery process in a tourist town. Sustainability, Vol. 10, No. 6, 1842p. https://doi.org/10.3390/su10061842   DOI
20 Baker, S. (1989). San Francisco in ruins: The 1906 serial photographs of George R. Lawrence. Landscape (Berkeley, Calif.), Vol. 30, No. 2, pp. 9-14.
21 Clark, D. G., Ford, J. D., and Tabish, T. (2018). What role can unmanned aerial vehicles play in emergency response in the Arctic: A case study from Canada. PLoS One, Vol. 13, No. 12, e0205299. https://doi.org/10.1371/journal.pone.0205299   DOI
22 Ding, J., Zhang, J., Zhan, Z., Tang, X., and Wang, X. (2022). A Precision Efficient Method for Collapsed Building Detection in Post-Earthquake UAV Images Based on the Improved NMS Algorithm and Faster R-CNN. Remote Sensing, Vol. 14, No. 3, 663p. https://doi.org/10.3390/rs14030663   DOI
23 Ghaffarian, S., Kerle, N., Pasolli, E., and Jokar Arsanjani, J. (2019). Post-disaster building database updating using automated deep learning: An integration of pre-disaster OpenStreetMap and multi-temporal satellite data. Remote sensing, Vol. 11, No. 20, 2427p. https://doi.org/10.3390/rs11202427   DOI
24 Jang, E., Kang, Y., Im, J., Lee, D. W., Yoon, J., and Kim, S. K. (2019). Detection and monitoring of forest fires using Himawari-8 geostationary satellite data in South Korea. Remote Sensing, Vol. 11, No. 3, 271p. https://doi.org/10.3390/rs11030271   DOI
25 Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2015). Region-based convolutional networks for accurate object detection and segmentation. IEEE transactions on pattern analysis and machine intelligence, Vol. 38, No. 1, pp. 142-158. https://doi.org/10.1109/tpami.2015.2437384   DOI
26 Groener, A., Chern, G., and Pritt, M. (2019). A comparison of deep learning object detection models for satellite imagery. In 2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), IEEE, pp. 1-10. https://doi.org/10.1109/aipr47015.2019.9174593   DOI
27 Gupta, R., Hosfelt, R., Sajeev, S., Patel, N., Goodman, B., Doshi, J., and Gaston, M. (2019). xbd: A dataset for assessing building damage from satellite imagery. arXiv preprint arXiv:1911.09296. https://doi.org/10.48550/arXiv.1911.09296   DOI
28 Jha, M. N., Levy, J., and Gao, Y. (2008). Advances in remote sensing for oil spill disaster management: state-of-the-art sensors technology for oil spill surveillance. Sensors, Vol. 8, No. 1, pp. 236-255. https://doi.org/10.3390/s8010236   DOI
29 Johnson, R. D. (1994). Change Vector Analysis for disaster assessment: a case study of Hurricane Andrew. Geocarto International, Vol. 9, No. 1, pp. 41-45. https://doi.org/10.1080/10106049409354440   DOI
30 Kim, J., Jeon, H., and Kim, D. J. (2020). Extracting flooded areas in southeast asia using SegNet and U-Net. Korean Journal of Remote Sensing, Vol. 36, No. 5_3, pp. 1095-1107. (in Korean with English abstract) https://doi.org/10.7780/kjrs.2020.36.5.3.8   DOI
31 Xue, B., Huang, B., Wei, W., Chen, G., Li, H., Zhao, N., and Zhang, H. (2021). An Efficient Deep-Sea Debris Detection Method Using Deep Neural Networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 14, pp. 12348-12360. https://doi.org/10.1109/jstars.2021.3130238   DOI
32 Li, P., Xu, H., and Song, B. (2011). A novel method for urban road damage detection using very high resolution satellite imagery and road map. Photogrammetric Engineering & Remote Sensing, Vol. 77, No. 10, pp. 1057-1066. https://doi.org/10.14358/pers.77.10.1057   DOI
33 Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779-788. https://doi.org/10.1109/cvpr.2016.91   DOI
34 Tatham, P. (2009). An investigation into the suitability of the use of unmanned aerial vehicle systems (UAVS) to support the initial needs assessment process in rapid onset humanitarian disasters. International journal of risk assessment and management, Vol. 13, No. 1, pp. 60-78. https://doi.org/10.1504/ijram.2009.026391   DOI
35 Ma, L., Li, M., Ma, X., Cheng, L., Du, P., and Liu, Y. (2017). A review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 130, pp. 277-293. https://doi.org/10.1016/j.isprsjprs.2017.06.001   DOI
36 Yamazaki, F., Kouchi, K. I., Matsuoka, M., Kohiyama, M., and Muraoka, N. (2004). Damage detection from highresolution satellite images for the 2003 Boumerdes, Algeria earthquake. In 13th World Conference on Earthquake Engineering, International Association for Earthquake Engineering, Vancouver, British Columbia, Canada, 13p.
37 Gang, S. M., Kim, D. R., Choung, Y. J., Park, J. S., Kim, J. M., and Jo, M. H. (2016). A plan for a prompt disaster response system using a 3D disaster management system based on high-capacity geographic and disaster information. Journal of the Korean Association of Geographic Information Studies, Vol. 19, No. 1, pp. 180-196. (in Korean with English abstract) https://doi.org/10.11108/kagis.2016.19.1.180   DOI
38 Lee, J., Im, J., Cha, D. H., Park, H., and Sim, S. (2019). Tropical cyclone intensity estimation using multi-dimensional convolutional neural networks from geostationary satellite data. Remote Sensing, Vol. 12, No. 1, 108p. https://doi.org/10.3390/rs12010108   DOI
39 Xu, J. Z., Lu, W., Li, Z., Khaitan, P., and Zaytseva, V. (2019). Building damage detection in satellite imagery using convolutional neural networks. arXiv preprint arXiv:1910.06444. https://doi.org/10.48550/arXiv.1910.06444   DOI