{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T09:41:13Z","timestamp":1766050873725,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T00:00:00Z","timestamp":1748476800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["42261075","41861062","20224ACB212003","20232BCJ22002","gpyc20240071","2022-02-04"],"award-info":[{"award-number":["42261075","41861062","20224ACB212003","20232BCJ22002","gpyc20240071","2022-02-04"]}]},{"DOI":"10.13039\/501100004479","name":"the Jiangxi Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["42261075","41861062","20224ACB212003","20232BCJ22002","gpyc20240071","2022-02-04"],"award-info":[{"award-number":["42261075","41861062","20224ACB212003","20232BCJ22002","gpyc20240071","2022-02-04"]}],"id":[{"id":"10.13039\/501100004479","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Jiangxi Provincial Training Project of Disciplinary, Academic, and Technical Leader","award":["42261075","41861062","20224ACB212003","20232BCJ22002","gpyc20240071","2022-02-04"],"award-info":[{"award-number":["42261075","41861062","20224ACB212003","20232BCJ22002","gpyc20240071","2022-02-04"]}]},{"name":"the Jiangxi Gan-Po Elite Talents\u2014Innovative High-End Talents Program","award":["42261075","41861062","20224ACB212003","20232BCJ22002","gpyc20240071","2022-02-04"],"award-info":[{"award-number":["42261075","41861062","20224ACB212003","20232BCJ22002","gpyc20240071","2022-02-04"]}]},{"name":"the State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, Chinese Academy of Surveying and Mapping","award":["42261075","41861062","20224ACB212003","20232BCJ22002","gpyc20240071","2022-02-04"],"award-info":[{"award-number":["42261075","41861062","20224ACB212003","20232BCJ22002","gpyc20240071","2022-02-04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>While deep learning-based image matching methods excel at extracting high-level semantic features from remote sensing data, their performance degrades significantly under cross-daylight conditions and wide-baseline geometric distortions, particularly in multi-source street-view scenarios. This paper presents a novel illumination-invariant framework that synergistically integrates geometric topology and semantic consistency to achieve robust multi-view matching for cross-daylight urban perception. We first design a self-supervised learning paradigm to extract illumination-agnostic features by jointly optimizing local descriptors and global geometric structures across multi-view images. To address extreme perspective variations, a homography-aware transformation module is introduced to stabilize feature representation under large viewpoint changes. Leveraging a graph neural network with hierarchical attention mechanisms, our method dynamically aggregates contextual information from both local keypoints and semantic topology graphs, enabling precise matching in occluded regions and repetitive-textured urban scenes. A dual-branch learning strategy further refines similarity metrics through supervised patch alignment and unsupervised spatial consistency constraints derived from Delaunay triangulation. Finally, a topology-guided multi-plane expansion mechanism propagates initial matches by exploiting the inherent structural regularity of street scenes, effectively suppressing mismatches while expanding coverage. Extensive experiments demonstrate that our framework outperforms state-of-the-art methods, achieving a 6.4% improvement in matching accuracy and a 30.5% reduction in mismatches under cross-daylight conditions. These advancements establish a new benchmark for reliable multi-source image retrieval and localization in dynamic urban environments, with direct applications in autonomous driving systems and large-scale 3D city reconstruction.<\/jats:p>","DOI":"10.3390\/ijgi14060212","type":"journal-article","created":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T09:47:34Z","timestamp":1748512054000},"page":"212","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Topology-Aware Multi-View Street Scene Image Matching for Cross-Daylight Conditions Integrating Geometric Constraints and Semantic Consistency"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9361-0219","authenticated-orcid":false,"given":"Haiqing","family":"He","sequence":"first","affiliation":[{"name":"School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China"},{"name":"Jiangxi Key Laboratory of Watershed Ecological Process and Information, East China University of Technology, Nanchang 330013, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5580-2799","authenticated-orcid":false,"given":"Wenbo","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China"}]},{"given":"Fuyang","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China"}]},{"given":"Zile","family":"He","sequence":"additional","affiliation":[{"name":"School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China"}]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China"}]},{"given":"Zhiyuan","family":"Sheng","sequence":"additional","affiliation":[{"name":"School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/s11263-007-0081-9","article-title":"3D Urban Scene Modeling Integrating Recognition and Reconstruction","volume":"78","author":"Cornelis","year":"2008","journal-title":"Int. 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