Building Change Detection in Earthquake: A Multi-Scale Interaction Network With Offset Calibration and A Dataset

As one of the most destructive natural disasters, earthquakes have struck many countries around the world in recent years, causing serious economic losses. Change detection (CD) can be applied to post-earthquake building CD as it can infer interested change regions from multi-temporal remote sensing images. Furthermore, the CD with short imaging interval will better satisfy the needs of the emergency rescues after earthquakes. However, the capability of current methods built on deep neural networks is limited because the dataset with short imaging interval is absent. To meet post-disaster immediate relief, we create a CD dataset, the Turkey earthquake CD dataset (TUE-CD), for the detection of building collapse in the short term after an earthquake. Due to the high requirement for timeliness of post-event images, the orbit of the satellite during post-event imaging deviates from that during pre-event imaging, which leads to a side-looking problem between bi-temporal images. To deal with these challenges, we present a multi-scale feature interaction network (MSI-Net) for efficient interaction between bi-temporal features, as well as mitigating the effect of side-looking problems. Specifically, the proposed MSI-Net consists of joint cross-attention (JCA) modules, multi-scale offset calibration (MOC) modules, and feature integration (FeI) modules. The JCA module unifies channel cross-attention and spatial joint attention for sufficient feature interaction. The MOC module further estimates the offsets to align the bi-temporal image with the multi-scale features. Finally, calibrated features and multi-scale features are fused by FeI modules for the prediction of changed areas. The best mF1 and mIoU scores are achieved on two public datasets and the constructed TUE-CD dataset: WHU-CD (95.58%, 91.81%), CLCD (82.96%, 73.53%), and TUE-CD (78.02%, 68.48%). Experimental results demonstrate that the proposed MSI-Net provides competitive performance compared to the state-of-the-art CD methods. The TUE-CD Dataset as well as the code of MSI-Net will be available at https://github.com/RSMagneto/MSI-Net.