{"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/d-linknet-linknet-with-pretrained-encoder-and","title":"D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction","arxiv_id":null,"date":"2018-12-08","proceeding":"CVPR 2018 2018 12","authors":["Lichen Zhou","Chuang Zhang","Ming Wu"],"abstract":"Road extraction is a fundamental task in the field of remote sensing which has been a hot research topic in the past\r\ndecade. In this paper, we propose a semantic segmentation\r\nneural network, named D-LinkNet, which adopts encoderdecoder structure, dilated convolution and pretrained encoder for road extraction task. The network is built with\r\nLinkNet architecture and has dilated convolution layers in\r\nits center part. Linknet architecture is efficient in computation and memory. Dilation convolution is a powerful tool\r\nthat can enlarge the receptive field of feature points without\r\nreducing the resolution of the feature maps. In the CVPR\r\nDeepGlobe 2018 Road Extraction Challenge, our best IoU\r\nscores on the validation set and the test set are 0.6466 and\r\n0.6342 respectively.","url_abs":"https://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w4/Zhou_D-LinkNet_LinkNet_With_CVPR_2018_paper.pdf","url_pdf":"https://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w4/Zhou_D-LinkNet_LinkNet_With_CVPR_2018_paper.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":"d-linknet-linknet-with-pretrained-encoder-and","repo_url":"https://github.com/2023-MindSpore-1/ms-code-25","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"d-linknet-linknet-with-pretrained-encoder-and","repo_url":"https://github.com/2023-MindSpore-4/Code3/tree/main/dlinknet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"d-linknet-linknet-with-pretrained-encoder-and","repo_url":"https://github.com/code-implementation1/Code1/tree/main/dlinknet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"d-linknet-linknet-with-pretrained-encoder-and","repo_url":"https://github.com/twigsWHy/dlinknet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"d-linknet-linknet-with-pretrained-encoder-and","repo_url":"https://github.com/yangyucheng000/dlinknet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"road-segementation","task_name":"Road Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/road-segementation-on-deepglobe","task":"Road Segmentation","dataset":"DeepGlobe","model":"D-LinkNet","rank_in_archive_order":2,"of":3,"metrics":{"IoU":"0.6412"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-bjroad","task":"Semantic Segmentation","dataset":"BJRoad","model":"D-LinkNet","rank_in_archive_order":7,"of":11,"metrics":{"IoU":"57.96"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-porto","task":"Semantic Segmentation","dataset":"Porto","model":"D-LinkNet","rank_in_archive_order":6,"of":6,"metrics":{"IoU":"70.20"},"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}