{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T21:14:56Z","timestamp":1779916496729,"version":"3.53.1"},"reference-count":44,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Geospatial Artificial Intelligence (GeoAI) enables the automated generation of built environment map features, such as building outlines\/footprints, on a global scale. However, the integration of these AI-generated datasets into Volunteered Geographic Information (VGI) platforms like OpenStreetMap (OSM) risks incorporating \u2018AI slop\u2019, consisting of geometrically inconsistent\/unreliable data, into the online map. While the OSM \u201cCode of Conduct for Automated Edits\u201d provides a policy framework for data ingestion, it lacks a machine-enforceable mechanism for real-time quality gating. This paper proposes a GeoAI-Gatekeeper to perform this task\u2014an automated process that applies empirical Acceptable Quality Thresholds (AQT) to address the GeoAI data governance problem. Because the Gatekeeper utilizes an intrinsic, no-reference evaluation of geometric fidelity, it can assess incoming AI-generated data streams in real-time without requiring ground-truth benchmarks. Importantly, it focuses exclusively on the geometric validation of building footprints, acknowledging for now that semantic enrichment, such as tagging, remains a human-centric task. The presented GeoAI-Gatekeeper is a working prototype developed for a specific urban area, systematically triaging incoming AI-generated data into three tiers; Auto-Accept, Manual Review, and Reject. It provides a Web-GIS interface for Human-in-the-Loop (HITL) functionality to ensure the OSM community remains the final arbiter of acceptable data quality. Testing the Gatekeeper in Dublin (Ireland) demonstrates that our solution can auto-ingest 93.6% of features with a 14x reduction in human review effort while still adhering to OSM\u2019s cartographic integrity standards. By implementing qualitative community guidelines into machine-enforceable thresholds, our approach introduces a viable methodology for next-generation hybrid VGI systems. Importantly, it ensures that the transition towards automated data ingestion reinforces, rather than undermines, the reliability of global crowd-source mapping datasets.<\/jats:p>","DOI":"10.3390\/ijgi15050217","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:17:23Z","timestamp":1779203843000},"page":"217","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Addressing GeoAI Governance: An Automated Gatekeeper for Building Outlines in OpenStreetMap"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9868-8338","authenticated-orcid":false,"given":"Lasith","family":"Niroshan","sequence":"first","affiliation":[{"name":"UCD School of Architecture, Planning and Environmental Policy, University College Dublin, D04 V1W8 Dublin, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4766-7297","authenticated-orcid":false,"given":"James D.","family":"Carswell","sequence":"additional","affiliation":[{"name":"School of Media, Technological University Dublin, D07 H6K8 Dublin, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1080\/13658816.2019.1684500","article-title":"GeoAI: Spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond","volume":"34","author":"Janowicz","year":"2020","journal-title":"Int. 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