MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos
Abstract
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.
Cite
Text
Zhu et al. "MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos." Winter Conference on Applications of Computer Vision, 2021.Markdown
[Zhu et al. "MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos." Winter Conference on Applications of Computer Vision, 2021.](https://mlanthology.org/wacv/2021/zhu2021wacv-msnet/)BibTeX
@inproceedings{zhu2021wacv-msnet,
title = {{MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos}},
author = {Zhu, Xiaoyu and Liang, Junwei and Hauptmann, Alexander},
booktitle = {Winter Conference on Applications of Computer Vision},
year = {2021},
pages = {2023-2032},
url = {https://mlanthology.org/wacv/2021/zhu2021wacv-msnet/}
}