{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T16:33:17Z","timestamp":1783096397439,"version":"3.54.6"},"reference-count":36,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T00:00:00Z","timestamp":1749686400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union \u2019s Horizon 2020 Research and Innovation Program","award":["963530"],"award-info":[{"award-number":["963530"]}]},{"name":"European Union \u2019s Horizon 2020 Research and Innovation Program","award":["03SF067"],"award-info":[{"award-number":["03SF067"]}]},{"name":"German Federal Ministry of Education and Research","award":["963530"],"award-info":[{"award-number":["963530"]}]},{"name":"German Federal Ministry of Education and Research","award":["03SF067"],"award-info":[{"award-number":["03SF067"]}]},{"name":"Council for Scientific and Industrial Research","award":["963530"],"award-info":[{"award-number":["963530"]}]},{"name":"Council for Scientific and Industrial Research","award":["03SF067"],"award-info":[{"award-number":["03SF067"]}]},{"name":"South African National Energy Development Institute","award":["963530"],"award-info":[{"award-number":["963530"]}]},{"name":"South African National Energy Development Institute","award":["03SF067"],"award-info":[{"award-number":["03SF067"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Accurate and detailed spatial data on wind energy infrastructure is essential for renewable energy planning, grid integration, and system analysis. However, publicly available datasets often suffer from limited spatial accuracy, missing attributes, and inconsistent metadata. To address these challenges, this study presents a harmonized and spatially refined dataset of wind turbines in South Africa, combining OpenStreetMap (OSM) data with high-resolution satellite imagery, deep learning-based coordinate correction, and manual curation. The dataset includes 1487 turbines across 42 wind farms, representing over 3.9 GW of installed capacity as of 2025. Of this, more than 3.6 GW is currently operational. The Geo-Coordinates were validated and corrected using a RetinaNet-based object detection model applied to both Google and Bing satellite imagery. Instead of relying solely on spatial precision, the curation process emphasized attribute completeness and consistency. Through systematic verification and cross-referencing with multiple public sources, the final dataset achieves a high level of attribute completeness and internal consistency across all turbines, including turbine type, rated capacity, and commissioning year. The resulting dataset is the most accurate and comprehensive publicly available dataset on wind turbines in South Africa to date. It provides a robust foundation for spatial analysis, energy modeling, and policy assessment related to wind energy development. The dataset is publicly available.<\/jats:p>","DOI":"10.3390\/ijgi14060232","type":"journal-article","created":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T11:47:07Z","timestamp":1749728827000},"page":"232","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Wind Turbines Dataset for South Africa: OpenStreetMap Data, Deep Learning Based Geo-Coordinate Correction and Capacity Analysis"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5947-3473","authenticated-orcid":false,"given":"Maximilian","family":"Kleebauer","sequence":"first","affiliation":[{"name":"Department of Energy Management and Power System Operation, University of Kassel, 34121 Kassel, Germany"},{"name":"Energy Meteorology and Geo Information System, Fraunhofer IEE, 34117 Kassel, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0033-4225","authenticated-orcid":false,"given":"Stefan","family":"Karamanski","sequence":"additional","affiliation":[{"name":"Energy Supply and Demand Group, Council for Scientific and Industrial Research, Stellenbosch 7600, South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Doron","family":"Callies","sequence":"additional","affiliation":[{"name":"Department of Energy Management and Power System Operation, University of Kassel, 34121 Kassel, Germany"},{"name":"Energy Meteorology and Geo Information System, Fraunhofer IEE, 34117 Kassel, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0857-6760","authenticated-orcid":false,"given":"Martin","family":"Braun","sequence":"additional","affiliation":[{"name":"Department of Energy Management and Power System Operation, University of Kassel, 34121 Kassel, Germany"},{"name":"Energy Meteorology and Geo Information System, Fraunhofer IEE, 34117 Kassel, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,12]]},"reference":[{"key":"ref_1","unstructured":"Global Wind Energy Council (2025, April 22). 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