{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T18:41:17Z","timestamp":1769193677982,"version":"3.49.0"},"reference-count":38,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T00:00:00Z","timestamp":1702252800000},"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"]}]},{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["963530"],"award-info":[{"award-number":["963530"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["03SF067"],"award-info":[{"award-number":["03SF067"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"name":"South African National Energy Development Institute (SANEDI)","award":["963530"],"award-info":[{"award-number":["963530"]}]},{"name":"South African National Energy Development Institute (SANEDI)","award":["03SF067"],"award-info":[{"award-number":["03SF067"]}]},{"name":"Department of Science and Innovation (DSI)","award":["963530"],"award-info":[{"award-number":["963530"]}]},{"name":"Department of Science and Innovation (DSI)","award":["03SF067"],"award-info":[{"award-number":["03SF067"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the realm of solar photovoltaic system image segmentation, existing deep learning networks focus almost exclusively on single image sources both in terms of sensors used and image resolution. This often prevents the wide deployment of such networks. Our research introduces a novel approach to train a network on a diverse range of image data, spanning UAV, aerial, and satellite imagery at both native and aggregated resolutions of 0.1 m, 0.2 m, 0.3 m, 0.8 m, 1.6 m, and 3.2 m. Using extensive hyperparameter tuning, we first determined the best possible parameter combinations for the network based on the DeepLabV3 ResNet101 architecture. We then trained a model using the wide range of different image sources. The final network offers several advantages. It outperforms networks trained with single image sources in multiple test applications as measured by the F1-Score (95.27%) and IoU (91.04%). The network is also able to work with a variety of target imagery due to the fact that a diverse range of image data was used to train it. The model is made freely available for further applications.<\/jats:p>","DOI":"10.3390\/rs15245687","type":"journal-article","created":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T13:18:21Z","timestamp":1702300701000},"page":"5687","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Multi-Resolution Segmentation of Solar Photovoltaic Systems Using Deep Learning"],"prefix":"10.3390","volume":"15","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":"Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), 34117 Kassel, Germany"}]},{"given":"Christopher","family":"Marz","sequence":"additional","affiliation":[{"name":"Council for Scientific and Industrial Research (CSIR), Pretoria 0184, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7476-3663","authenticated-orcid":false,"given":"Christoph","family":"Reudenbach","sequence":"additional","affiliation":[{"name":"Environmental Informatics, Faculty of Geography, Philipps-University Marburg, 35032 Marburg, Germany"}]},{"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":"Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), 34117 Kassel, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/S1364-0321(99)00011-8","article-title":"Renewable energy and sustainable development: A crucial review","volume":"4","author":"Dincer","year":"2000","journal-title":"Renew. 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