用深度学习快速估算城市街面温度，助力缓解热岛效应。
A Machine Learning Approach for the Efficient Estimation of Ground-Level Air Temperature in Urban Areas
- 用图像到图像的深度网络关联城市空间与气象数据
- 比传统数值模型更快且更省计算资源
- 适合城市规划与气候韧性研究者使用
21世纪日益密集的城市面临可持续与韧性发展的挑战，其中城市热岛（UHI）现象加剧了热应力，阻碍了目标实现。准确估算街面空气温度有助于识别需优先改进的区域以降低热不适。本文探索了图像到图像的深度神经网络（DNNs）在关联城市空间与气象变量与街面温度之间的有效性。针对特定应用场景，实现了街面温度的时空估计，并与现有成熟数值模型进行对比。结果表明，深度神经网络在计算效率上显著优于数值模型，是地面气温估算的更快速、更低耗方案。
原文摘要 · Abstract (English)
The increasingly populated cities of the 21st Century face the challenge of being sustainable and resilient spaces for their inhabitants. However, climate change, among other problems, makes these objectives difficult to achieve. The Urban Heat Island (UHI) phenomenon that occurs in cities, increasing their thermal stress, is one of the stumbling blocks to achieve a more sustainable city. The ability to estimate temperatures with a high degree of accuracy allows for the identification of the highest priority areas in cities where urban improvements need to be made to reduce thermal discomfort. In this work we explore the usefulness of image-to-image deep neural networks (DNNs) for correlating spatial and meteorological variables of a urban area with street-level air temperature. The air temperature at street-level is estimated both spatially and temporally for a specific use case, and compared with existing, well-established numerical models. Based on the obtained results, deep neural networks are confirmed to be faster and less computationally expensive alternative for ground-level air temperature compared to numerical models.
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