Operational earthquake-induced building damage assessment using CNN-based direct remote sensing change detection on superpixel level

Accurate and quick building damage assessment is an indispensable step after a destructive earthquake. Acquiring building damage information of the seismic area in a remotely sensed way enables a timely emergency response. Existing remote sensing building damage detection methods based on convolutional neural network (CNN) mainly need two-step processing or only use single post-event image, leading to low efficiency and inaccurate building boundary. Considering the practical needs of emergency rescue and post-disaster reconstruction, this study proposed a hierarchical building damage assessment workflow using CNN-based direct remote sensing change detection on superpixel level. First, vulnerable building areas close to the epicenter are extracted using extra feature enhancement bands (EFEBs) to narrow the extent of image processing. Then, fine scale building damage is detected in the extracted building areas based on a direct change detection method with pre-event superpixel constraint (PreSC) strategy to improve the precision and efficiency. Finally, a rapid remote sensing earthquake damage index (rRSEDI) is used to quantitatively assess the damage. Experimental results of the case study show that damaged buildings can be effectively and accurately localized and classified using the proposed workflow. Comparative experiments with single-temporal image and post-event segmentation further embody the superiority of the direct change detection. The damage assessment result matches the official report after Ludian earthquake, proving the reliability of the proposed workflow. For future natural hazard events, the workflow can contribute to formulating appropriate disaster management, prevention and mitigation policies.