Thermal performance assessment of building(s) is an essential process for optimal energy management, heat-loss evaluation, and energy audit applications. Such an assessment can help foresee the requirements for future intervention(s) and aid in benchmarking energy performance. This paper provides a review of several thermal performance assessment techniques and a broad classification based on measurement types, methods, and applications. Moreover, the article provides a comprehensive survey of various quantitative indices utilized for practical heat-loss assessment of building elements. This paper’s unique contribution is the proposed three-layer framework that details the handling and processing of UAS-based thermal imagery for heat loss quantification. Primarily, the novelty of this work lies in the application of instance segmentation technique (e.g., Mask R–CNN) to compute the thermal transmittance values (e.g., U-values) for various objects (e.g., doors, walls, windows, and facades). To the best of our knowledge, this research work is first-of-its-kind using a sizeable thermal data repository (e.g. 100,000 augmented images). Multiple standard U-values are analyzed for windows and walls and compared with The American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE) building standards. The preliminary results of Mask-RCNN from over 100,000 trained (including augmented) images from multiple campus buildings yield the following performance metrics: 1) provides an Average Precision (AP) of 0.67 (windows) and 0.46 (facades); and 2) Intersection of Union (IoU) of 0.05 (windows) and 0.5 (facades) respectively. Moreover, the U-values are consistently close enough to the ASHRAE standards in distinguishing window types (e.g. 0.77 for single-pane windows and 0.38 for double-pane windows).

建筑物的热性能评估是实现最佳能源管理，热损失评估和能源审核应用的重要过程。这样的评估可以帮助预见未来干预的要求，并有助于对能源绩效进行基准测试。本文概述了几种热性能评估技术，并根据测量类型，方法和应用对它进行了广泛的分类。此外，本文对用于建筑构件实际热损失评估的各种定量指标进行了全面的调查。本文的独特贡献是所提出的三层框架，该框架详细介绍了基于UAS的热成像的处理和处理以进行热量损失量化。首先，这项工作的新颖之处在于应用实例分割技术（例如Mask R–CNN）来计算各种物体（例如门，墙壁，窗户和外墙）的热透射率值（例如U值）。据我们所知，这项研究工作是首次使用大型热数据仓库（例如100,000张增强图像）进行。分析窗户和墙壁的多个标准U值，并将其与美国供热，制冷和空调工程师协会（ASHRAE）的建筑标准进行比较。根据来自多个校园建筑的100,000幅经过训练（包括增强）的图像得出的Mask-RCNN的初步结果得出以下性能指标：1）提供0.67（窗​​口）和0.46（立面）的平均精度（AP）；和2）联合的相交（IoU）为0.05（窗口）和0。5（立面）。此外，在区分窗口类型时，U值始终足够接近ASHRAE标准（例如，单窗格窗口为0.77，双窗格窗口为0.38）。