DeepDamageNet: A two-step deep-learning model for multi-disaster building damage segmentation and classification using satellite imagery
<|reference_start|>DeepDamageNet: A two-step deep-learning model for multi-disaster building damage segmentation and classification using satellite imagery: 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 results submitted as part of the xView2 Challenge, a competition to design better models for identifying buildings and their damage level after exposure to multiple kinds of natural disasters. Our best model couples a building identification semantic segmentation convolutional neural network (CNN) to a building damage classification CNN, with a combined F1 score of 0.66, surpassing the xView2 challenge baseline F1 score of 0.28. We find that though our model was able to identify buildings with relatively high accuracy, building damage classification across various disaster types is a difficult task due to the visual similarity between different damage levels and different damage distribution between disaster types, highlighting the fact that it may be important to have a probabilistic prior estimate regarding disaster damage in order to obtain accurate predictions.<|reference_end|>
@article{alisjahbana2024deepdamagenet:,
    title={DeepDamageNet: A two-step deep-learning model for multi-disaster
  building damage segmentation and classification using satellite imagery},
    author={Irene Alisjahbana, Jiawei Li, Ben (Mullet) Strong, Yue Zhang},
    journal={arXiv preprint arXiv:2405.04800},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04800},
    primaryClass={cs.CV cs.LG}
}