Natural hazards such as earthquakes, hurricanes, and wildfires are increasing in frequency and severity due to climate change and urban expansion. Earth Observation (EO) technologies offer critical capabilities for assessing disaster impacts rapidly and at scale. In this chapter, we introduce state-of-the-art approaches that leverage Synthetic Aperture Radar (SAR), Interferometric SAR (InSAR), and optical imagery, as well as emerging multi-modal methods that fuse these datasets with citizen science data and geophysical models to support rapid natural hazard damage assessment. We review the challenges and solutions in extracting reliable disaster information from diverse remote sensing products, including artificial intelligence (AI)-based, causality-informed, and physics-informed frameworks. Emphasis is placed on how EO data can be used not only for post-event assessment but also for real time decision making and long term resilience planning. The chapter also identifies key research gaps, such as uncertainty quantification, data fusion standardization, and model interpretability, and outlines future directions for advancing EO-based hazard monitoring and impact assessment. This overview provides a comprehensive understanding of the current landscape and ongoing development in EO-driven multi-hazard damage assessment, bridging both research and practice.
Li et al. (Tue,) studied this question.