Overcoming Optical Blindness in Tropical Earth Systems: A Cloud-Based Sentinel-1 Big Data and Explainable AI Framework for Flood Susceptibility Mapping
Sep 2026· Remote Sensing in Earth Systems Sciences· Vol 9· 0 citations· 46 references
Flood Risk Assessment and Management
TL;DR
A cloud-based, explainable framework that integrates big-data processing of Sentinel-1 Synthetic Aperture Radar on Google Earth Engine with tree-based bagging and boosting ensembles in the Takkalasi watershed, Indonesia provides a reproducible and transferable basis for ecosystem-based flood adaptation in data-scarce tropical watersheds.
Sentinel-1 C-band SAR interferometric coherence for Burned-area detection of the 2017 “Las Máquinas” megafire is evaluated, indicating that the Burned and Unburned classes are close to linearly separable in the coherence feature space.
Miguel Aguilera, A. Cabrera-Ariza, Paulina Vidal-Páez et al.· Remote Sensing· 0 citations
Floods in monsoon-dominated river basins exhibit high spatio-temporal variability, necessitating high-resolution, multi-sensor approaches for reliable monitoring and impact assessment. In flood-prone agricultural regions, continuous monitoring using optical remote sensing is frequently hindered by dense monsoon cloud c...
Informal settlements represent a major urban challenge in rapidly expanding cities, yet their identification from Earth Observation (EO) data remains difficult because of their heterogeneous appearance and incomplete official inventories. This work presents a multi-sensor deep learning (DL) framework for slum-likelihoo...
Luigi Russo, A. Ferral, S. Ullo et al.· 0 citations
Multi-source remote sensing fusion is widely assumed to improve soil salinity (SSC) inversion accuracy. However, its actual effectiveness in complex tidal flat environments remains insufficiently validated. This study evaluated five fusion schemes (single-source Sentinel-2, single-source GF-2, S2+S1, GF-2+S1, and all-s...
Jia-Xin Wang, Xiu-Jun Guo· Journal of Physics, Conferen...· 0 citations
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
Microsoft Research Blog· microsoft.comOct 22, 2026
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
