Hot Hém: Sài Gòn Giũa Cái Nóng Hông Còng Bàng -- Saigon in Unequal Heat

• URL: http://arxiv.org/abs/2512.11896v1
• Date: Wed, 10 Dec 2025 05:10:09 GMT
• Title: Hot Hém: Sài Gòn Giũa Cái Nóng Hông Còng Bàng -- Saigon in Unequal Heat
• Authors: Tessa Vu,
• Abstract summary: Hot Hém is a GeoAI workflow that estimates pedestrian heat exposure in H Ch Minh City (HCMC), Videt Nam, colloquially known as Si Gn.<n>This spatial data science pipeline combines Google Street View (GSV) imagery, semantic image segmentation, and remote sensing.<n>Two XGBoost models are trained to predict land surface temperature (LST) using a GSV training dataset in selected administrative wards, known as phng, and are deployed in a patchwork manner across all OSMnx-derived pedestrian network nodes to enable heat-aware routing.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pedestrian heat exposure is a critical health risk in dense tropical cities, yet standard routing algorithms often ignore micro-scale thermal variation. Hot Hém is a GeoAI workflow that estimates and operationalizes pedestrian heat exposure in Hô Chí Minh City (HCMC), Vi\d{e}t Nam, colloquially known as Sài Gòn. This spatial data science pipeline combines Google Street View (GSV) imagery, semantic image segmentation, and remote sensing. Two XGBoost models are trained to predict land surface temperature (LST) using a GSV training dataset in selected administrative wards, known as phŏng, and are deployed in a patchwork manner across all OSMnx-derived pedestrian network nodes to enable heat-aware routing. This is a model that, when deployed, can provide a foundation for pinpointing where and further understanding why certain city corridors may experience disproportionately higher temperatures at an infrastructural scale.

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