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
MIT professors Adam Berinsky and Chara Podimata discuss a new project to track how AI chatbots are responding to questions about the 2026 U.S. midterm elections.
Microsoft Research Blog· microsoft.comOct 30, 2026
Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances. The post From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence appeared first on Microsoft Research.
Critical flaws, compromised release channels and agentic AI threats landed within days of each other in mid-October 2026. Why “Black October” may say more about how well we now find weaknesses than about the attacks themselves. The post Black October? The Week AI Changed the Cybersecurity Conversation appeared first on GPT-Lab.
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