{"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/building-extraction-from-remote-sensing-1","title":"Building Extraction from Remote Sensing Images via an Uncertainty-Aware Network","arxiv_id":"2307.12309","date":"2023-07-23","proceeding":null,"authors":["wei he","Jiepan Li","Weinan Cao","Liangpei Zhang","Hongyan zhang"],"abstract":"Building extraction aims to segment building pixels from remote sensing images and plays an essential role in many applications, such as city planning and urban dynamic monitoring. Over the past few years, deep learning methods with encoder-decoder architectures have achieved remarkable performance due to their powerful feature representation capability. Nevertheless, due to the varying scales and styles of buildings, conventional deep learning models always suffer from uncertain predictions and cannot accurately distinguish the complete footprints of the building from the complex distribution of ground objects, leading to a large degree of omission and commission. In this paper, we realize the importance of uncertain prediction and propose a novel and straightforward Uncertainty-Aware Network (UANet) to alleviate this problem. To verify the performance of our proposed UANet, we conduct extensive experiments on three public building datasets, including the WHU building dataset, the Massachusetts building dataset, and the Inria aerial image dataset. Results demonstrate that the proposed UANet outperforms other state-of-the-art algorithms by a large margin.","url_abs":"https://arxiv.org/abs/2307.12309v1","url_pdf":"https://arxiv.org/pdf/2307.12309v1.pdf","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":[{"paper_slug":"building-extraction-from-remote-sensing-1","repo_url":"https://github.com/henryjiepanli/uncertainty-aware-network","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"extracting-buildings-in-remote-sensing-images","task_name":"Extracting Buildings In Remote Sensing Images"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/extracting-buildings-in-remote-sensing-images-4","task":"Extracting Buildings In Remote Sensing Images","dataset":"Massachusetts building dataset","model":"UANet(VGG-16)","rank_in_archive_order":3,"of":3,"metrics":{"IoU":"76.41"},"uses_additional_data":false},{"leaderboard":"/sota/extracting-buildings-in-remote-sensing-images-2","task":"Extracting Buildings In Remote Sensing Images","dataset":"WHU Building Dataset","model":"UANet(VGG-16)","rank_in_archive_order":6,"of":7,"metrics":{"F1":"95.91","IoU":"92.15"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-inria-aerial-image","task":"Semantic Segmentation","dataset":"INRIA Aerial Image Labeling","model":"UANet(PVT-V2-B2)","rank_in_archive_order":1,"of":8,"metrics":{"IoU":"83.34"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-inria-aerial-image","task":"Semantic Segmentation","dataset":"INRIA Aerial Image Labeling","model":"UANet(Re2sNet50)","rank_in_archive_order":2,"of":8,"metrics":{"IoU":"83.17"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-inria-aerial-image","task":"Semantic Segmentation","dataset":"INRIA Aerial Image Labeling","model":"UANet(VGG-16)","rank_in_archive_order":3,"of":8,"metrics":{"IoU":"83.08"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-inria-aerial-image","task":"Semantic Segmentation","dataset":"INRIA Aerial Image Labeling","model":"UANet(ResNet50)","rank_in_archive_order":6,"of":8,"metrics":{"IoU":"82.17"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2307.12309","atlas_url":"https://app.syntology.ai/?focus=2307.12309","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}