MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos
This addresses the problem of efficient post-disaster damage assessment for emergency responders, though it is incremental as it builds on existing instance segmentation methods.
The paper tackles building damage assessment from aerial videos by introducing a new dataset of social media aerial videos with instance-level damage masks and proposing MSNet, a model with novel region proposal designs and unsupervised score refinement, achieving state-of-the-art results on their dataset.
In this paper, we study the problem of efficiently assessing building damage after natural disasters like hurricanes, floods or fires, through aerial video analysis. We make two main contributions. The first contribution is a new dataset, consisting of user-generated aerial videos from social media with annotations of instance-level building damage masks. This provides the first benchmark for quantitative evaluation of models to assess building damage using aerial videos. The second contribution is a new model, namely MSNet, which contains novel region proposal network designs and an unsupervised score refinement network for confidence score calibration in both bounding box and mask branches. We show that our model achieves state-of-the-art results compared to previous methods in our dataset. We will release our data, models and code.