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

A new multi-sensor deep learning framework integrating PlanetScope multispectral, COSMO-SkyMed SAR, and PRISMA hyperspectral imagery achieved the best slum-likelihood mapping performance in Córdoba, Argentina using late fusion with hyperspectral support, according to an arXiv preprint (arXiv:2608.28680v1). The study found that ReNaBaP settlements exhibit significantly higher surface temperatures during heatwaves, suggesting multi-sensor Earth Observation fusion can aid urban vulnerability mapping.

arXiv:2608.28680v1 Announce Type: new Abstract: 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-likelihood mapping in C\'ordoba, Argentina, integrating high-resolution PlanetScope multispectral MS imagery, COSMO-SkyMed CSK Synthetic Aperture Radar SAR data, and medium-resolution PRISMA hyperspectral HS observations. The problem is formulated as a patch-level classification task using the official Registro Nacional de Barrios Populares ReNaBaP inventory as reference, and the models are evaluated through four geographically partitioned folds. SAR-only and MS-only baselines, their configurations with PRISMA HS support, and early fusion EF , middle fusion MF , and late fusion LF strategies are systematically compared. Results show that LF+HS provides the best overall balance between classification performance and spatial selectivity, while PRISMA contributes complementary spectral information alongside the higher-resolution MS and SAR representations. Beyond the standard evaluation against ReNaBaP, an external municipal vulnerability layer is used to interpret detections outside the official polygons, showing that several apparent false positives overlap broader vulnerable urban areas. Thermal analysis further shows that ReNaBaP settlements exhibit significantly higher surface temperatures than their immediate surroundings during a heatwave event, indicating localised surface-heat amplification. Taken together, these results suggest that multi-sensor EO fusion can support both the mapping of ReNaBaP settlements and the interpretation of broader urban vulnerability patterns.