Hot H\'em: S\`ai G\`on Gi\~ua C\'ai N\'ong H\^ong C\`ong B\`ang -- Saigon in Unequal Heat

TL;DR

This paper presents Hot Hém, a spatial data science workflow that estimates pedestrian heat exposure in Saigon using Google Street View, remote sensing, and machine learning to enable heat-aware routing in dense tropical cities.

Contribution

It introduces a novel GeoAI pipeline combining GSV imagery, semantic segmentation, and temperature modeling for urban heat exposure analysis.

Findings

Models predict land surface temperature with high accuracy.

Heat-aware routing can identify high-temperature city corridors.

The workflow supports urban planning for heat mitigation.

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.