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The accurate extraction of building damage after destructive natural disasters is critical for disaster rescue and assessment. To achieve a rapid disaster response, training a model from scratch using enough ground-truth data collected in situ is not feasible. Often, in disaster situations, it is ineffective to directly apply an existing model due...
It is of great significance for emergency rescue to rapidly assess damage of buildings after an earthquake. Some previous methods are time-consuming, data are difficult to obtain, or there is lack of regional damage assessment. We proposed a novel way to rapidly assess building damage by comprehensively utilizing earth observation-derived data and...
Semantic and instance segmentation methods are commonly used to build extraction from high-resolution images. The semantic segmentation method involves assigning a class label to each pixel in the image, thus ignoring the geometry of the building rooftop, which results in irregular shapes of the rooftop edges. As for instance segmentation, there is...
Higher resolution building mapping from lower resolution remote sensing images is in great demand due to the lack of higher resolution data access, especially in the context of disaster assessment. High resolution building layout map is crucial for emergency rescue after the disaster. The emergency response time would be reduced if detailed buildin...