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Existing methods mainly focus on achieving content consistency in common geometric transformation and illumination change by projecting the local information around the interest lines into the descriptor space. However, we find that in practice the asymmetry in local descriptor regions caused by incomplete extraction or occlusion continually occurs and leads to the loss of local information, which cannot be retrieved through projection and destroys the content consistency. Based on this finding, we propose an endpoint\u2010aware line segment matching method with geometry\u2010based prediction (GPMatcher) to predict the projected positions of endpoint pairs of line fragments and directly find the most matched line. By constructing content and geometric associations between endpoints and the global image, endpoints that are inexactly regarded as corresponding can be accurately reassigned. Experiment on three representative datasets demonstrates that our method outperforms state\u2010of\u2010the\u2010art in both indoor and outdoor scenarios under a more rigorous evaluation based on downstream tasks.<\/jats:p>","DOI":"10.1111\/tgis.70287","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T09:42:27Z","timestamp":1780911747000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Endpoint\u2010Aware Line Segment Matching With Geometry\u2010Based Prediction"],"prefix":"10.1111","volume":"30","author":[{"given":"Zhuang","family":"Yuan","sequence":"first","affiliation":[{"name":"The State Key Lab. LIESMARS Wuhan University  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9783-3030","authenticated-orcid":false,"given":"Xianwei","family":"Zheng","sequence":"additional","affiliation":[{"name":"The State Key Lab. 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