用街景图和机器学习为胡志明市行人定制热暴露地图，助力避暑路线规划。
Hot Hém: Sài Gòn Giũa Cái Nóng Hông Còng Bàng -- Saigon in Unequal Heat
- 结合街景图与语义分割，训练模型预测地表温度。
- 在10个行政区部署分块模型，覆盖全城市人行网络节点。
- 适合城市规划者和健康研究者，用于识别高温高风险路段。
行人热暴露是密集热带城市中的重大健康风险，但标准路径算法常忽略微尺度温差。Hot Hém 是一项基于地理人工智能的流程，用于估算并实现胡志明市（越南，俗称西贡）的行人热暴露。该空间数据科学管道整合谷歌街景（GSV）影像、语义图像分割与遥感数据。在选定的行政区域（称为phŏng）中，使用GSV数据集训练两个XGBoost模型以预测地表温度（LST），并以拼接方式部署于所有OSMnx生成的人行网络节点，实现热感知路径规划。该模型可为精准定位城市特定通道在基础设施层面为何出现显著升温提供基础。
原文摘要 · Abstract (English)
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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