{"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/spatial-information-inference-net-road","title":"Spatial Information Inference Net: Road Extraction Using Road-Specific Contextual Information","arxiv_id":null,"date":"2019-10-28","proceeding":"ISPRS Journal of Photogrammetry and Remote Sensing 2019 10","authors":["Chao Tao","Ji Qi","Yansheng Li","Hao Wang","Haifeng Li"],"abstract":"Deep neural networks perform well in road extraction from very high-resolution satellite imagery. A network with certain reasoning ability will give more satisfactory road network extraction results. In this study, we designed a spatial information inference structure, which enables multidirectional message passing between pixels when it is integrated to a typical semantic segmentation framework. Since the spatial information could be propagated and reinforced via inter layer propagation, the proposed road extraction network can learn both the local visual characteristics of the road and the global spatial structure information (such as the continuity and trend of the road). As a result, this method can effectively solve occlusions and preserve the continuity of the extracted road. The validation experiments using three large datasets of very high-resolution (VHR) satellite imagery show that the proposed method can improve road extraction accuracy and provide an output that is more in line with human expectations. Keywords: Road extraction, Semantic segmentation, Spatial information inference structure, Road-specific contextual information","url_abs":"https://doi.org/10.1016/j.isprsjprs.2019.10.001","url_pdf":"https://doi.org/10.1016/j.isprsjprs.2019.10.001","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":"spatial-information-inference-net-road","repo_url":"https://github.com/ErenTuring/SIINet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"road-segementation","task_name":"Road Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}