부산대학교 도서관

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 informed governmental policy decisions. Current remote sensing based approaches to near real-time natural hazard mapping increasingly leverage advantage of deep learning (DL). Nevertheless, DL-based approaches are mainly designed for one specific task in a single geographic region based on specific frequency bands of satellite data. For that reason, 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 that this methodology improved generalizability across four U-Net architectures for the segmentation of unseen natural hazards, such as flood events, landslides, and massive glacier collapses. Importantly, our method is strongly invariant to geographic differences and the type of input frequency bands of satellite data. That is confirmed by obtaining a balanced accuracy of up to 0.74 in comparison with performance of reference baselines. By leveraging characteristics of unlabeled images from the target domain that are publicly available, our approach is able to further improve the generalization behavior of DL models without fine-tuning. That is reflected in performance metrics. Thereby, our approach is one of first attempts to support the development of foundation models for earth monitoring with the objective of directly segmenting unseen natural hazards across novel geographic regions from different sources of satellite imagery.