DeepDamageNet: A two-step deep-learning model for multi-disaster building damage segmentation and classification using satellite imagery
This addresses the problem of time-consuming and inaccurate manual damage assessment for disaster response teams, though it is incremental as it builds on existing deep-learning approaches.
The paper tackled automating building damage assessment from satellite imagery by developing a two-step deep-learning model for segmentation and classification, achieving a combined F1 score of 0.66, which surpassed the baseline of 0.28 in the xView2 Challenge.
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.