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An Instance Segmentation and Clustering Model for Energy Audit Assessments in Built Environments: A Multi-Stage ApproachHeat loss quantification (HLQ) is an essential step in improving a building’s thermal performance and optimizing its energy usage. While this problem is well-studied in the literature, most of the existing studies are either qualitative or minimally driven quantitative studies that rely on localized building envelope points and are, thus, not suitable for automated solutions in energy audit applications. This research work is an attempt to fill this gap of knowledge by utilizing intensive thermal data (on the order of 100,000 plus images) and constitutes a relatively new area of analysis in energy audit applications. Specifically, we demonstrate a novel process using deep-learning methods to segment more than 100,000 thermal images collected from an unmanned aerial system (UAS). To quantify the heat loss for a building envelope, multiple stages of computations need to be performed: object detection (using Mask-RCNN/Faster R-CNN), estimating the surface temperature (using two clustering methods), and finally calculating the overall heat transfer coefficient (e.g., the U-value). The proposed model was applied to eleven academic campuses across the state of North Dakota. The preliminary findings indicate that Mask R-CNN outperformed other instance segmentation models with an mIOU of 73% for facades, 55% for windows, 67% for roofs, 24%more »