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This paper addresses this issue by modeling\n                    <jats:italic>Navigation\u2010Lost<\/jats:italic>\n                    (NL) scenes as structured interactions between human and environmental factors. We propose the\n                    <jats:italic>Navigation\u2010Lost Knowledge Graph<\/jats:italic>\n                    (NLKG), a conceptual framework that incorporates 24 entity types and 27 relation types, covering subjective perception, travel modes, and objective environmental context (e.g., weather, terrain, road layout). To efficiently populate the NLKG at scale, we develop a triple extraction model\u2014\n                    <jats:italic>NavLTR<\/jats:italic>\n                    \u2014which includes modules for handling nested semantics, hierarchical relations, and urban functional categories. To mitigate data scarcity and anchor evaluation, we create the\n                    <jats:italic>Navigation\u2010Lost Information Extraction Database<\/jats:italic>\n                    (NIED), a comprehensive domain corpus. On extensive benchmarks, NavLTR achieves state\u2010of\u2010the\u2010art performance, with an F1 score of 92.82%, significantly outperforming existing baselines. We demonstrate the practical utility of NLKG in two downstream applications: (i) a prototype question\u2010answering system that surfaces contextual cues to assist navigators and (ii) multi\u2010perspective graph analytics for risk prediction and factor attribution, providing actionable insights for urban wayfinding and spatial design. Collectively, these contributions\u2014the schema, corpus, and schema\u2010aware extractor\u2014lay the foundation for a reproducible framework for NL scene understanding and offer a practical path for transforming unstructured narratives into context\u2010aware geospatial reasoning, benefiting both end users and urban planners.\n                  <\/jats:p>","DOI":"10.1111\/tgis.70192","type":"journal-article","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T03:45:29Z","timestamp":1769053529000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Understanding Navigation\u2010Lost Scenes Based on Knowledge Graphs Using a Deep Learning Approach"],"prefix":"10.1111","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6707-6865","authenticated-orcid":false,"given":"Lin","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer Science China University of Geosciences  Wuhan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science China University of Geosciences  Wuhan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zejun","family":"Zuo","sequence":"additional","affiliation":[{"name":"School of Computer Science China University of Geosciences  Wuhan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science China University of Geosciences  Wuhan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingxia","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer Science China University of Geosciences  Wuhan China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2021.103076"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1177\/0963721410383243"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gsf.2022.101412"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2025.105913"},{"key":"e_1_2_11_6_1","doi-asserted-by":"crossref","unstructured":"Dai X. 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