DeepDamageNet: A two-step deep-learning model for multi-disaster building damage segmentation and classification using satellite imagery

arXiv:2405.04800v1 Announce Type: cross
Abstract: Satellite imagery has played an increasingly important role in post-disaster building damage assessment. Unfortunately, current methods still rely on manual visual interpretation, which is often time-consuming and can cause very low accuracy. To address the limitations of manual interpretation, there has been a significant increase in efforts to automate the process. We present a solution that performs the two most important tasks in building damage assessment, segmentation and classification, through deep-learning models. We show our …

abstract accuracy arxiv assessment building classification cs.cv cs.lg current disaster interpretation limitations low role satellite segmentation type visual