{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T02:09:43Z","timestamp":1769566183035,"version":"3.49.0"},"reference-count":57,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Crowdsourced geospatial platforms constitute complex socio-technical systems in which data quality and reliability emerge from collective user behavior rather than centralized control. This study proposes a system-oriented, unsupervised machine learning framework to assess the reliability of crowdsourced building data using only intrinsic indicators. The framework is demonstrated through a large-scale analysis of OpenStreetMap building polygons in Tehran. Six intrinsic indicators\u2014reflecting contributor activity, temporal dynamics, semantic instability, and geometric evolution\u2014were normalized using fuzzy membership functions and objectively weighted based on their discriminative influence within a K-means clustering process. Five reliability classes were identified, ranging from very low to very high reliability. The resulting classification exhibited strong internal validity (average silhouette coefficient = 0.58) and pronounced spatial coherence (Global Moran\u2019s I = 0.85, p &lt; 0.001). This approach eliminates dependence on authoritative reference datasets, enabling scalable, reproducible, and feature-level reliability assessment in open geospatial systems. The framework provides a transferable methodological foundation for trust-aware analysis and decision-making in participatory and data-intensive systems.<\/jats:p>","DOI":"10.3390\/systems14020129","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T11:35:42Z","timestamp":1769513742000},"page":"129","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A System-Oriented Framework for Reliability Assessment of Crowdsourced Geospatial Data Using Unsupervised Learning"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3382-4233","authenticated-orcid":false,"given":"Hussein Hamid","family":"Hassan","sequence":"first","affiliation":[{"name":"School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran P.O. Box 14155-6619, Iran"},{"name":"Department of Spatial Planning, College of City and Regional Planning, University of Duhok, Zakho Street 38, Duhok 1006 AJ, Kurdistan Region, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7133-3844","authenticated-orcid":false,"given":"Rahim","family":"Ali Abbaspour","sequence":"additional","affiliation":[{"name":"School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran P.O. Box 14155-6619, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alireza","family":"Chehreghan","sequence":"additional","affiliation":[{"name":"Faculty of Mining Engineering, Sahand University of Technology, Tabriz P.O. Box 51335-1996, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"See, L., Mooney, P., Foody, G., Bastin, L., Comber, A., Estima, J., Fritz, S., Kerle, N., Jiang, B., and Laakso, M. (2016). Crowdsourcing, citizen science or volunteered geographic information? The current state of crowdsourced geographic information. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5050055"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s10708-011-9423-9","article-title":"GeoWeb and crisis management: Issues and perspectives of volunteered geographic information","volume":"78","author":"Roche","year":"2013","journal-title":"GeoJournal"},{"key":"ref_3","unstructured":"Gray, S.J. (2023). Enhancing Geospatial Data: Collecting and Visualising User-Generated Content Through Custom Toolkits and Cloud Computing Workflows. 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