{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ai-based-thermal-bridge-detection-of-building","title":"AI-based thermal bridge detection of building rooftops on district scale using aerial images","arxiv_id":null,"date":"2021-08-09","proceeding":"EG-ICE Workshop on Intelligent Computing in Engineering 2021 8","authors":["Zoe Mayer","Yu Hou","James Kahn","Rebekka Volk","Frank Schultmann"],"abstract":"Thermal bridges are weak areas of building envelopes that conduct more heat to the outside than surrounding envelope areas. They lead to increased energy consumption and the formation of mold. With a neural network approach, we demonstrate a method of automatically detecting thermal bridges on building rooftops from panorama drone images of whole city districts. To train the neural network, we created a dataset including 917 images and 6895 annotations. The images in the dataset contain thermal information for detecting thermal bridges and a height map for rooftop recognition in addition to regular RGB information. Due to the small dataset, our approach currently only has an average recall of 9.4% @IoU:0.5-0.95 (14.4% for large objects). Nevertheless, our approach reliably detects structures only on rooftops and not on other parts of buildings, without any additional segmentation effort of building parts.","url_abs":"https://publikationen.bibliothek.kit.edu/1000136256","url_pdf":"https://publikationen.bibliothek.kit.edu/1000136256/123066859","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"2d-object-detection","task_name":"2D Object Detection"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"object-detection","task_name":"Object Detection"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"mask-r-cnn","method_name":"Mask R-CNN"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"roi-align","method_name":"RoIAlign"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/instance-segmentation-on-tbbr","task":"Instance Segmentation","dataset":"TBBR","model":"Wahn Mask R-CNN","rank_in_archive_order":5,"of":5,"metrics":{"Average Recall@IoU:0.5-0.95":"9.4"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}