Hot H'em: S`ai G`on Gi~ua C'ai N'ong Hong C`ong B`ang -- Saigon in Unequal Heat
Not on the map. Why?
Abstract (arXiv)
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 Ho Ch'i Minh City (HCMC), Videt 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 phuong, 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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 19 references that are in the map. First posted on arXiv 10 Dec 2025.
Builds on
5 of 19 references.
References · 5
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
- 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,351
- 2016OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networkscited by 110
- 2023Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidarcited by 60
- 2025GlobalBuildingAtlas: An Open Global and Complete Dataset of Building Polygons, Heights and LoD1 3D Modelscited by 16
Built on by
Not cited yet.