This work presents the analysis of semantically segmented, longitudinally, and spatially rich thermal images collected at the neighborhood scale to identify hot and cool spots in urban areas. An infrared observatory was operated over a few months to collect thermal images of different types of buildings on the educational campus of the National University of Singapore. A subset of the thermal image dataset was used to train state-of-the-art deep learning models to segment various urban features such as buildings, vegetation, sky, and roads. It was observed that the U-Net segmentation model with `resnet34' CNN backbone has the highest mIoU score of 0.99 on the test dataset, compared to other models such as DeepLabV3, DeeplabV3+, FPN, and PSPnet. The masks generated using the segmentation models were then used to extract the temperature from thermal images and correct for differences in the emissivity of various urban features. Further, various statistical measure of the temperature extracted using the predicted segmentation masks is shown to closely match the temperature extracted using the ground truth masks. Finally, the masks were used to identify hot and cool spots in the urban feature at various instances of time. This forms one of the very few studies demonstrating the automated analysis of thermal images, which can be of potential use to urban planners for devising mitigation strategies for reducing the urban heat island (UHI) effect, improving building energy efficiency, and maximizing outdoor thermal comfort.

翻译：本研究提出了一种基于邻域尺度采集的、具有纵向与空间丰富性的热图像语义分割分析方法，用于识别城市区域中的热点与冷点。通过红外观测站在数月内对新加坡国立大学教育园区内不同类型建筑进行热图像采集，利用部分热图像数据集训练当前最先进的深度学习模型，以分割建筑物、植被、天空、道路等城市特征要素。研究发现，采用resnet34卷积神经网络骨干的U-Net分割模型在测试数据集上的平均交并比最高达0.99，优于DeepLabV3、DeepLabV3+、FPN及PSPnet等其他模型。利用分割模型生成的掩膜提取热图像温度数据，并校正不同城市特征要素发射率差异。进一步分析表明，基于预测分割掩膜提取的温度统计指标与基于真实掩膜提取的温度高度吻合。最终，通过掩膜识别城市特征要素在不同时刻的热点与冷点分布。该研究是极少数实现热图像自动化分析的成果之一，可为城市规划者制定缓解城市热岛效应策略、提升建筑能效及优化户外热舒适度提供参考。