Post-disaster Recovery Monitoring based on Multi-Source Remote Sensing Imagery and Deep Learning

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First, multi-temporal post-disaster remote sensing imagery and the ground-truth of changing has been collected.Then, with the help of Multi-task Urban Mapping, the land cover semantic segmentation, object detection, and DSM in multi-temporal can be generated.After that, an end-to-end deep learning model for change detection has been trained by data fusing and ensemble learning. Meanwhile, to solve the slight misalignment of multi-temporal imagery as well as the imbalanced land cover ratio, a specific loss function will be proposed.Finally, the trained change detection model is capable of detecting land cover changes, such as the destroyed, new vacant, under construction, completed, etc.