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arXiv Computer Vision By Luigi Russo, Anabella Ferral, Silvia Liberata Ullo, Paolo Gamba

Multi-Sensor Mapping of Vulnerable Urban Settlements Using SAR, Multispectral, and Hyperspectral Imagery: A Case Study in C\'ordoba, Argentina

Read the original on arXiv Computer Vision →

The paper introduces a multi‑sensor deep learning framework for mapping informal settlements in Córdoba, Argentina, using high‑resolution PlanetScope multispectral imagery, COSMO‑SkyMed SAR data, and medium‑resolution PRISMA hyperspectral observations. It compares SAR‑only, MS‑only, and various fusion strategies (early, middle, late) and finds that late fusion with hyperspectral data (LF+HS) delivers the best balance of classification accuracy and spatial precision. The study also demonstrates that detections outside official polygons align with broader municipal vulnerability layers and that identified settlements show higher surface temperatures during a heatwave, highlighting localized heat amplification.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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