We explore the implementation of deep learning techniques for precise building damage assessment in the context of natural hazards, utilizing remote sensing data. The xBD dataset, comprising diverse disaster events from across the globe, serves as the primary focus, facilitating the evaluation of deep learning models. We tackle the challenges of generalization to novel disasters and regions while accounting for the influence of low-quality and noisy labels inherent in natural hazard data. Furthermore, our investigation quantitatively establishes that the minimum satellite imagery resolution essential for effective building damage detection is 3 meters and below 1 meter for classification using symmetric and asymmetric resolution perturbation analyses. To achieve robust and accurate evaluations of building damage detection and classification, we evaluated different deep learning models with residual, squeeze and excitation, and dual path network backbones, as well as ensemble techniques. Overall, the U-Net Siamese network ensemble with F-1 score of 0.812 performed the best against the xView2 challenge benchmark. Additionally, we evaluate a Universal model trained on all hazards against a flood expert model and investigate generalization gaps across events, and out of distribution from field data in the Ahr Valley. Our research findings showcase the potential and limitations of advanced AI solutions in enhancing the impact assessment of climate change-induced extreme weather events, such as floods and hurricanes. These insights have implications for disaster impact assessment in the face of escalating climate challenges.

翻译：我们探索了利用深度学习技术，基于遥感数据对自然灾害中的建筑损伤进行精确评估。本研究以包含全球多种灾害事件的xBD数据集为主要研究对象，系统评估了深度学习模型的表现。我们解决了模型对新灾害类型和新区域的泛化难题，同时考虑了自然灾害数据中低质量标签和噪声标签的影响。此外，通过对称与非对称分辨率扰动分析，我们定量确定了有效建筑损伤检测所需的最低卫星影像分辨率为3米，而分类任务所需分辨率需低于1米。为实现建筑损伤检测与分类的稳健准确评估，我们评估了采用残差网络、压缩激励网络与双路径网络骨干的多种深度学习模型及集成技术。总体而言，基于U-Net孪生网络的集成模型在xView2挑战赛基准测试中表现最优，F-1分数达0.812。我们还对比了基于所有灾害训练的通用模型与针对洪水的专家模型，探究了跨灾害事件的泛化差距，以及阿尔河谷实地数据分布外推的局限性。研究结果揭示了先进人工智能解决方案在增强气候变化引发的极端天气事件（如洪水和飓风）影响评估中的潜力与局限，为应对日益严峻的气候挑战中的灾害影响评估提供了重要启示。