BDHE-Net: A Novel Building Damage Heterogeneity Enhancement Network for Accurate and Efficient Post-Earthquake Assessment Using Aerial and Remote Sensing Data
Source abstract
Abstract
Accurate and efficient post-earthquake building damage assessment methods enable key building damage information to be obtained more quickly after an earthquake, providing strong support for rescue and reconstruction efforts. Although many methods have been proposed, most have limited effect on accurately extracting severely damaged and collapsed buildings, and they cannot meet the needs of emergency response and rescue operations. Therefore, in this paper, we develop a novel building damage heterogeneity enhancement network for pixel-level building damage classification of post-earthquake unmanned aerial vehicle (UAV) and remote sensing data. The proposed BDHE-Net includes the following three modules: a data augmentation module (DAM), a building damage attention module (BDAM), and a multilevel feature adaptive fusion module (MFAF), which are used to alleviate the weight deviation of intact and slightly damaged categories during model training, pay attention to the heterogeneous characteristics of damaged buildings, and enhance the extraction of house integrity contour information at different resolutions of the image. In addition, a combined loss function is used to focus more attention on the small number of severely damaged and collapsed classes. The proposed model was tested on remote sensing and UAV images acquired from the Afghanistan and Baoxing earthquakes, and the combined loss function and the role of the three modules were studied. The results show that compared with the state-of-the-art methods, the proposed BDHE-Net achieves the best results, with an F1 score improvement of 6.19–8.22%. By integrating the DBA, BDAM, and MFAF modules and combining the loss functions, the model’s classification accuracy for severely damaged and collapsed categories can be improved.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote-Sensing Image Classification · Remote Sensing and Land Use · Video Surveillance and Tracking Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
National Earthquake Response Support Service, Beijing 100049, China
National Disaster Reduction Center of China, Ministry of Emergency Management of China, Beijing 100124, China
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
Funding
the National Key R&D Program of China · the National Natural Science Foundation of China · the Natural Science and Technology Foundation of Guizhou Province
References (32)
Show source-provided references
1. Using high-resolution satellite images for post-earthquake building damage assessment: A study following the 26 January 2001 Gujarat earthquake10.1193/1.1650865 ↗
2. Field survey report and satellite image interpretation of the 2013 Super Typhoon Haiyan in the Philippines10.5194/nhess-15-805-2015 ↗
3. Gong, L., Li, Q., and Zhang, J. (2013, January 21–26). Earthquake building damage detection with object-oriented change detection. Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS, Melbourne, Australia.10.1109/IGARSS.2013.6723627 ↗
4. Lipton, Z.C., Berkowitz, J., and Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning. arXiv.
5. Gradient-based learning applied to document recognition10.1109/5.726791 ↗
6. A gentle introduction to graph neural networks
7. Duarte, D., Nex, F., Kerle, N., and Vosselman, G. (2018). Multi-resolution feature fusion for image classification of building damages with convolutional neural networks. Remote Sens., 10.10.3390/rs10101636 ↗
8. Chowdhury, T., Murphy, R., and Rahnemoonfar, M. (2022). RescueNet: A high resolution UAV semantic segmentation benchmark dataset for natural disaster damage assessment. arXiv.10.1109/IGARSS47720.2021.9553712 ↗
9. Damaged building detection from post-earthquake remote sensing imagery considering heterogeneity characteristics10.1109/TGRS.2022.3200872 ↗
10. Gupta, R., and Shah, M. (2021, January 10–15). RescueNet: Joint building segmentation and damage assessment from satellite imagery. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.10.1109/ICPR48806.2021.9412295 ↗
11. U-Net: Multiscale convolutional neural network with cross-directional attention for building damage assessment from satellite images
12. Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made disasters10.1016/j.rse.2021.112636 ↗
13. BCD-Net: Building change detection based on fully scale connected U-Net and subpixel convolution10.1080/01431161.2023.2285737 ↗
14. Bai, Y., Mas, E., and Koshimura, S. (2018). Towards operational satellite-based damage-mapping using u-net convolutional network: A case study of 2011 tohoku earthquake-tsunami. Remote Sens., 10.10.3390/rs10101626 ↗
15. Rudner, T.G., Rußwurm, M., Fil, J., Pelich, R., Bischke, B., Kopacková, V., and Bilinski, P. (2018, January 6). Rapid computer vision-aided disaster response via fusion of multiresolution, multisensor, and multitemporal satellite imagery. Proceedings of the First Workshop on AI for Social Good. Neural Information Processing Systems (NIPS-2018), Montreal, QC, Canada.
16. Hong, Z., Yang, Y., Liu, J., Jiang, S., Pan, H., Zhou, R., Zhang, Y., Han, Y., Wang, J., and Yang, S. (2022). Enhancing 3D reconstruction model by deep learning and its application in building damage assessment after earthquake. Appl. Sci., 12.10.3390/app12199790 ↗
17. Hong, Z., Zhong, H., Pan, H., Liu, J., Zhou, R., Zhang, Y., Han, Y., Wang, J., Yang, S., and Zhong, C. (2022). Classification of building damage using a novel convolutional neural network based on post-disaster aerial images. Sensors, 22.10.3390/s22155920 ↗
18. Fast building detection using new feature sets derived from a very high-resolution image, digital elevation and surface model10.1080/01431161.2024.2313991 ↗
19. Yu, Z., Chen, Z., Sun, Z., Guo, H., Leng, B., He, Z., Yang, J., and Xing, S. (2022). SegDetector: A Deep Learning Model for Detecting Small and Overlapping Damaged Buildings in Satellite Images. Remote Sens., 14.10.3390/rs14236136 ↗
20. Insights on the June 21, 2022, Khost earthquake, Afghanistan10.1007/s12046-023-02215-y ↗
21. Deep subpixel mapping based on semantic information modulated network for urban land use mapping10.1109/TGRS.2021.3050824 ↗
22. Spatial pyramid pooling in deep convolutional networks for visual recognition10.1109/TPAMI.2015.2389824 ↗
23. Multi-directional geodesic neural networks via equivariant convolution10.1145/3272127.3275102 ↗
24. Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv.
25. Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8–14). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01234-2_49 ↗
26. Liu, S., Huang, D., and Wang, Y. (2019). Learning spatial fusion for single-shot object detection. arXiv.
27. Milletari, F., Navab, N., and Ahmadi, S.-A. (2016, January 25–28). V-net: Fully convolutional neural networks for volumetric medical image segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.10.1109/3DV.2016.79 ↗
28. Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P. (2017, January 22–29). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.324 ↗
29. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). Pytorch: An imperative style, high-performance deep learning library. arXiv.
30. Xie, S., Girshick, R., Dollár, P., Tu, Z., and He, K. (2017, January 21–26). Aggregated residual transformations for deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.634 ↗
31. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
32. Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5–9). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany.10.1007/978-3-319-24574-4_28 ↗