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.

翻译：21世纪日益拥挤的城市面临着成为居民可持续且具有韧性空间的挑战。然而，气候变化等问题使得这些目标难以实现。城市中发生的城市热岛效应加剧了热应力，是实现更可持续城市的主要障碍之一。高精度估算温度的能力有助于识别城市中需优先进行城市改造以缓解热不适的区域。本研究探讨了图像到图像的深度神经网络在关联城市区域空间与气象变量同街道层面气温方面的有效性。针对具体用例，在空间和时间维度上估算了街道层面的气温，并与现有成熟的数值模型进行了比较。基于所得结果，深度神经网络被证实为相较于数值模型更快速且计算成本更低的地面气温估算替代方案。