{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T05:13:22Z","timestamp":1778562802418,"version":"3.51.4"},"reference-count":41,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T00:00:00Z","timestamp":1778284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009988","name":"Huzhou Municipal Science and Technology Bureau","doi-asserted-by":"crossref","award":["2024GZ54"],"award-info":[{"award-number":["2024GZ54"]}],"id":[{"id":"10.13039\/501100009988","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100018542","name":"Natural Science Foundation of Sichuan Province","doi-asserted-by":"crossref","award":["2026NSFSC0219"],"award-info":[{"award-number":["2026NSFSC0219"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Road networks are fundamental components of transportation infrastructure and play a crucial role in various geospatial applications. Although deep learning-based semantic segmentation models have achieved promising results in extracting roads from high-resolution remote sensing imagery, the resulting networks often suffer from topological fragmentation due to occlusions and shadows. To address this issue, we propose a topology-aware road extraction method that integrates deep learning-based segmentation with a graph-based connectivity refinement strategy. Specifically, a Pyramid Scene Parsing Network (PSPNet) is first employed to generate initial road probability maps. Subsequently, a connectivity-oriented post-processing pipeline is introduced, which incorporates a multi-source cost function strategy and a direction-aware Dijkstra search algorithm. By utilizing endpoint tangent vectors as inertial weights, the algorithm effectively reconstructs fragmented segments while ensuring geometric smoothness and topological consistency. Furthermore, a dynamic road width restoration strategy is applied to transform refined skeletons into physically consistent road entities. Experiments conducted on two publicly available datasets, CHN6-CUG and DeepGlobe, demonstrate the effectiveness of the proposed method. Quantitative results show that the refinement process significantly enhances road connectivity with a minimal trade-off in pixel-level accuracy. Specifically, the Conn metric increases by 0.1989 on the CHN6-CUG dataset and 0.3055 on the DeepGlobe dataset, while MIoU remains high with only marginal decreases of 1.07% and 0.45%, respectively. These findings indicate that the method effectively restores structural continuity, helping with reliable road network generation and subsequent integration into Geographic Information System (GIS)-based applications such as urban planning and autonomous navigation.<\/jats:p>","DOI":"10.3390\/ijgi15050208","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T13:10:42Z","timestamp":1778505042000},"page":"208","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Topology-Aware Road Extraction from Remote Sensing Images Using Deep Learning and Graph-Based Connectivity Refinement"],"prefix":"10.3390","volume":"15","author":[{"given":"Zixuan","family":"Teng","sequence":"first","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5615-5015","authenticated-orcid":false,"given":"Zezhong","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"The Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6619-9097","authenticated-orcid":false,"given":"Xiangyang","family":"Sun","sequence":"additional","affiliation":[{"name":"SHU-SUCG Research Centre for Building Industrialization, SILC Business School, Shanghai University, Shanghai 201800, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Geographical & Earth Science, University of Glasgow, Glasgow G12 8QQ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Qi, H., Shi, J., Chen, J., Chi, C., and Shan, H. 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