Climate change results in an increased probability of extreme weather events that put societies and businesses at risk on a global scale. Therefore, near real-time mapping of natural hazards is an emerging priority for the support of natural disaster relief, risk management, and informing governmental policy decisions. Recent methods to achieve near real-time mapping increasingly leverage deep learning (DL). However, DL-based approaches are designed for one specific task in a single geographic region based on specific frequency bands of satellite data. Therefore, DL models used to map specific natural hazards struggle with their generalization to other types of natural hazards in unseen regions. In this work, we propose a methodology to significantly improve the generalizability of DL natural hazards mappers based on pre-training on a suitable pre-task. Without access to any data from the target domain, we demonstrate this improved generalizability across four U-Net architectures for the segmentation of unseen natural hazards. Importantly, our method is invariant to geographic differences and differences in the type of frequency bands of satellite data. By leveraging characteristics of unlabeled images from the target domain that are publicly available, our approach is able to further improve the generalization behavior without fine-tuning. Thereby, our approach supports the development of foundation models for earth monitoring with the objective of directly segmenting unseen natural hazards across novel geographic regions given different sources of satellite imagery.

翻译：气候变化导致极端天气事件发生概率增加，使全球范围内的社会和商业面临风险。因此，近实时自然灾害测绘已成为支持自然灾害救援、风险管理以及为政府决策提供信息的新兴重点领域。近期实现近实时测绘的方法越来越多地利用深度学习。然而，基于深度学习的方法通常针对特定地理区域的单一任务，并依赖于特定频段的卫星数据。因此，用于测绘特定自然灾害的深度学习模型难以泛化至其他类型的自然灾害以及未观测区域。本研究提出一种方法，通过基于合适预任务进行预训练，显著提升深度学习自然灾害测绘模型的泛化能力。在无需任何目标域数据的情况下，我们展示了该方法在四种U-Net架构上对未观测自然灾害分割任务的泛化性能提升。关键之处在于，我们的方法对地理差异及卫星数据频段类型差异具有不变性。通过利用目标域中公开可用的未标注图像特征，本方法能在无需微调的情况下进一步改善泛化行为。由此，本方法支持开发面向地球监测的基础模型，其目标是在不同卫星影像数据源下，对跨新地理区域的未观测自然灾害进行直接分割。