  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.
