Abstract:Informal settlements face disproportionate exposure to climate-related health hazards. However, existing methodologies lack systematic approaches to link diverse settlement characteristics with environmental health outcomes. We develop a data-driven framework to assess heat vulnerability in Rio de Janeiro's favelas by combining spatially-constrained clustering with land surface temperature (LST) analysis. Using remote sensing and geospatial features, we identify two distinct favela typologies: recent, well-connected settlements on flat terrain (Cluster 0) and historical, poorly-connected communities on vegetated slopes (Cluster 1). Analysis of 16 extreme heat events reveals systematic temperature differences of 2--3$^\circ$C between clusters, with flat-terrain favelas experiencing significantly higher heat exposure. Our findings demonstrate that settlement morphology critically influences heat vulnerability, providing a replicable framework for targeted urban planning and public health interventions in informal settlements globally.
| Comments: | Workshop Publication (ICLR ML4RS 2026) |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.26133 [cs.LG] |
| (or arXiv:2604.26133v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.26133 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Baptiste Clemence [view email]
[v1]
Tue, 28 Apr 2026 21:45:22 UTC (23,097 KB)