Abstract
Pedestrian heat exposure is a critical health risk in dense tropical cities, yet standard routing algorithms often ignore micro-scale thermal variation. Hot H\'em is a GeoAI workflow that estimates and operationalizes pedestrian heat exposure in H\^o Ch\'i Minh City (HCMC), Vi\d{e}t Nam, colloquially known as S\`ai G\`on. 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 ph\u{o}ng, 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.