{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T15:31:29Z","timestamp":1781537489658,"version":"3.54.5"},"reference-count":43,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T00:00:00Z","timestamp":1755043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Vision-based navigation is a common solution for the critical challenge of GPS-denied Unmanned Aerial Vehicle (UAV) operation, but a research gap remains in the autonomous discovery of robust landmarks from aerial survey imagery needed for such systems. In this work, we propose a framework to fill this gap by identifying visually distinctive urban buildings from aerial survey imagery and curating them into a landmark database for GPS-free UAV localization. The proposed framework constructs semantically rich embeddings using intermediate layers from a pre-trained YOLOv11n-seg segmentation network. This novel technique requires no additional training. An unsupervised landmark selection strategy, based on the Isolation Forest algorithm, then identifies objects with statistically unique embeddings. Experimental validation on the VPAIR aerial-to-aerial benchmark shows that the proposed max-pooled embeddings, assembled from selected layers, significantly improve retrieval performance. The top-1 retrieval accuracy for landmarks more than doubled compared to typical buildings (0.53 vs. 0.31), and a Recall@5 of 0.70 is achieved for landmarks. Overall, this study demonstrates that unsupervised outlier selection in a carefully constructed embedding space yields a highly discriminative, computation-friendly set of landmarks suitable for real-time, robust UAV navigation.<\/jats:p>","DOI":"10.3390\/make7030081","type":"journal-article","created":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T09:43:01Z","timestamp":1755078181000},"page":"81","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Unsupervised Knowledge Extraction of Distinctive Landmarks from Earth Imagery Using Deep Feature Outliers for Robust UAV Geo-Localization"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4644-3587","authenticated-orcid":false,"given":"Zakhar","family":"Ostrovskyi","sequence":"first","affiliation":[{"name":"Department of Computer Science, Khmelnytskyi National University, 11 Instytuts\u2019ka Str., 29016 Khmelnytskyi, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0739-9678","authenticated-orcid":false,"given":"Oleksander","family":"Barmak","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Khmelnytskyi National University, 11 Instytuts\u2019ka Str., 29016 Khmelnytskyi, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3609-112X","authenticated-orcid":false,"given":"Pavlo","family":"Radiuk","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Khmelnytskyi National University, 11 Instytuts\u2019ka Str., 29016 Khmelnytskyi, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8043-0785","authenticated-orcid":false,"given":"Iurii","family":"Krak","sequence":"additional","affiliation":[{"name":"Department of Theoretical Cybernetics, Taras Shevchenko National University of Kyiv, 4d Akademika Glushkova Ave., 03680 Kyiv, Ukraine"},{"name":"Laboratory of Communicative Information Technologies, V.M. Glushkov Institute of Cybernetics, 40 Akademika Glushkova Ave., 03187 Kyiv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Javaid, S., Khan, M.A., Fahim, H., He, B., and Saeed, N. (2025). Explainable AI and monocular vision for enhanced UAV navigation in smart cities: Prospects and challenges. Front. Sustain. Cities, 7.","DOI":"10.3389\/frsc.2025.1561404"},{"key":"ref_2","first-page":"e70085","article-title":"Model predictive control for autonomous UAV landings: A comprehensive review of strategies, applications and challenges","volume":"2025","author":"Panjavarnam","year":"2025","journal-title":"J. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hua, W., Chen, Q., and Chen, W. (2024). A new lightweight network for efficient UAV object detection. Sci. 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