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In this paper, we study the problem of efficiently assessing building damageafter natural disasters like hurricanes, floods or fires, through aerial videoanalysis. We make two main contributions. The first contribution is a newdataset, consisting of user-generated aerial videos from social media withannotations of instance-level building damage masks. This provides the firstbenchmark for quantitative evaluation of models to assess building damage usingaerial videos. The second contribution is a new model, namely MSNet, whichcontains novel region proposal network designs and an unsupervised scorerefinement network for confidence score calibration in both bounding box andmask branches. We show that our model achieves state-of-the-art resultscompared to previous methods in our dataset. We will release our data, modelsand code.

MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos