Abstract
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.
Abstract (translated)
卫星影像在灾后建筑损害评估中扮演着越来越重要的角色。然而，目前的评估方法仍然依赖于人工视觉解释，这通常需要花费大量时间，并可能导致非常低的精度。为解决手动解释的局限性，已经加大了自动化过程的努力。我们提出了一个解决方案，通过深度学习模型执行建筑损害评估中的两个最重要的任务：分割和分类。我们在xView2挑战中展示了我们的结果，该挑战旨在为识别在多种自然灾害中受损的建筑和其损害程度提供更好的模型。我们最好的模型将具有建筑识别的语义分割卷积神经网络（CNN）与建筑损害分类卷积神经网络相结合，F1分数为0.66，超过了xView2挑战基线F1分数0.28。我们发现，尽管我们的模型能够以相对较高的准确度识别建筑物，但不同灾害类型之间建筑损害的分类仍然具有困难，因为不同灾害类型的损害水平和损害分布之间存在视觉相似性，这表明在获得准确预测之前，关于灾害损害的概率先验估计可能是重要的。
URL
https://arxiv.org/abs/2405.04800
