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.

翻译：行人热暴露是热带高密度城市中一项关键的健康风险，然而标准路径规划算法往往忽略了微观尺度的热环境变化。Hot Hém 是一个地理人工智能工作流，用于估算并应用于越南胡志明市（俗称西贡）的行人热暴露评估。该空间数据科学流程整合了谷歌街景图像、语义图像分割与遥感技术。通过选取行政坊（phŏng）中的谷歌街景训练数据集，训练了两个 XGBoost 模型以预测地表温度，并以拼图方式部署于所有基于 OSMnx 生成的行人网络节点上，从而实现热感知路径规划。该模型在部署后，可为精准定位城市廊道在基础设施尺度上为何经历不成比例的高温现象提供分析基础，并促进对其成因的深入理解。