Flood Depth Mapping from SAR Imagery Using CS-Mamba with DEM Sensitivity Analysis

We compiled an inventory of building condition (destroyed vs. survived) following the 2024 Noto Peninsula earthquake for the entire affected area, totaling over 140 000 structures. We discuss how we fused freely available data of different types and from different sources to generate the final dataset. We show that our method produces highly accurate results relative to those obtained by on-site surveys. These data can be used to train AI to quickly detect damaged structures in future disasters.

Informal occupation of unused lands for settlements is a critical issue in Peru. In most cases, such areas are unsafe against natural hazards. We performed a time-series analysis of Sentinel-1 images at recent informal settlements in Lima. The result suggests that a low-cost and sustainable monitoring system of informal settlements can be implemented.