{"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/geoseg-a-computer-vision-package-for","title":"Geoseg: A Computer Vision Package for Automatic Building Segmentation and Outline Extraction","arxiv_id":"1809.03175","date":"2018-09-10","proceeding":null,"authors":["Guangming Wu","Zhiling Guo"],"abstract":"Recently, deep learning algorithms, especially fully convolutional network\nbased methods, are becoming very popular in the field of remote sensing.\nHowever, these methods are implemented and evaluated through various datasets\nand deep learning frameworks. There has not been a package that covers these\nmethods in a unifying manner. In this study, we introduce a computer vision\npackage termed Geoseg that focus on building segmentation and outline\nextraction. Geoseg implements over nine state-of-the-art models as well as\nutility scripts needed to conduct model training, logging, evaluating and\nvisualization. The implementation of Geoseg emphasizes unification, simplicity,\nand flexibility. The performance and computational efficiency of all\nimplemented methods are evaluated by comparison experiment through a unified,\nhigh-quality aerial image dataset.","url_abs":"http://arxiv.org/abs/1809.03175v1","url_pdf":"http://arxiv.org/pdf/1809.03175v1.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":"geoseg-a-computer-vision-package-for","repo_url":"https://github.com/huster-wgm/geoseg","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"geoseg-a-computer-vision-package-for","repo_url":"https://github.com/GeoVision-Lab/Geoseg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}