{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T08:00:25Z","timestamp":1771747225621,"version":"3.50.1"},"reference-count":36,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T00:00:00Z","timestamp":1681430400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013091","name":"Science and Technology Major Project of Guangxi","doi-asserted-by":"publisher","award":["AA22068072"],"award-info":[{"award-number":["AA22068072"]}],"id":[{"id":"10.13039\/501100013091","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Applied Earth Observation and Geoinformation"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1016\/j.jag.2023.103309","type":"journal-article","created":{"date-parts":[[2023,4,22]],"date-time":"2023-04-22T20:56:11Z","timestamp":1682196971000},"page":"103309","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":20,"special_numbering":"C","title":["PVNet: A novel semantic segmentation model for extracting high-quality photovoltaic panels in large-scale systems from high-resolution remote sensing imagery"],"prefix":"10.1016","volume":"119","author":[{"given":"Jianxun","family":"Wang","sequence":"first","affiliation":[]},{"given":"Xin","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Weicheng","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Li","family":"Hua","sequence":"additional","affiliation":[]},{"given":"Junyi","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Haigang","family":"Sui","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.jag.2023.103309_b0005","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.solener.2016.06.069","article-title":"Review of photovoltaic power forecasting","volume":"136","author":"Antonanzas","year":"2016","journal-title":"Sol. Energy."},{"key":"10.1016\/j.jag.2023.103309_b0010","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.jag.2023.103309_b0015","doi-asserted-by":"crossref","DOI":"10.1038\/sdata.2016.106","article-title":"Distributed solar photovoltaic array location and extent dataset for remote sensing object identification","volume":"3","author":"Bradbury","year":"2016","journal-title":"Sci. Data"},{"key":"10.1016\/j.jag.2023.103309_b0020","article-title":"A global database of power plants","volume":"18","author":"Byers","year":"2018","journal-title":"World Resources Institute"},{"key":"10.1016\/j.jag.2023.103309_b0025","unstructured":"Camilo, J., Wang, R., Collins, L.M., Bradbury, K., Malof, J.M., 2018. Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery. arXiv preprint arXiv:1801.04018."},{"key":"10.1016\/j.jag.2023.103309_b0030","first-page":"1","article-title":"Linknet: Exploiting encoder representations for efficient semantic segmentation","author":"Chaurasia","year":"2017","journal-title":"IEEE Vis. Commun. Image Process (VCIP)"},{"key":"10.1016\/j.jag.2023.103309_b0035","doi-asserted-by":"crossref","first-page":"2697","DOI":"10.3390\/rs14112697","article-title":"Extraction of photovoltaic plants using machine learning methods: A case study of the pilot energy city of Golmud","volume":"14","author":"Chen","year":"2022","journal-title":"China. Remote Sens."},{"key":"10.1016\/j.jag.2023.103309_b0040","first-page":"833","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","author":"Chen","year":"2018","journal-title":"Eur. Conf. Comput. Vis. (ECCV)"},{"key":"10.1016\/j.jag.2023.103309_b0045","doi-asserted-by":"crossref","first-page":"4211","DOI":"10.3390\/rs14174211","article-title":"A Hierarchical information extraction method for large-scale centralized photovoltaic power plants based on multi-source remote sensing images","volume":"14","author":"Ge","year":"2022","journal-title":"Remote Sens."},{"key":"10.1016\/j.jag.2023.103309_b0050","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016","journal-title":"IEEE Conf. Comput. Vis. Patt. Recogn. (CVPR)"},{"key":"10.1016\/j.jag.2023.103309_b0055","unstructured":"Hou, X., Wang, B., Hu, W., Yin, L., Wu, H., 2019. SolarNet: a deep learning framework to map solar power plants in China from satellite imagery. arXiv preprint arXiv:1912.03685."},{"key":"10.1016\/j.jag.2023.103309_b0060","doi-asserted-by":"crossref","first-page":"5389","DOI":"10.5194\/essd-13-5389-2021","article-title":"Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery","volume":"13","author":"Jiang","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"10.1016\/j.jag.2023.103309_b0065","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1038\/s41586-021-03957-7","article-title":"A global inventory of photovoltaic solar energy generating units","volume":"598","author":"Kruitwagen","year":"2021","journal-title":"Nature"},{"key":"10.1016\/j.jag.2023.103309_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.adapen.2021.100057","article-title":"Understanding rooftop PV panel semantic segmentation of satellite and aerial images for better using machine learning","volume":"4","author":"Li","year":"2021","journal-title":"Adv. Appl. Energy"},{"key":"10.1016\/j.jag.2023.103309_b0075","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1038\/s43017-021-00244-x","article-title":"Challenges and opportunities for carbon neutrality in China","volume":"3","author":"Liu","year":"2022","journal-title":"Nat. Rev. Earth Environ."},{"key":"10.1016\/j.jag.2023.103309_b0080","series-title":"Int Conf. Renew. Energy Res. Appl","first-page":"1428","article-title":"Automatic Solar Photovoltaic Panel Detection in Satellite Imagery","author":"Malof","year":"2015"},{"key":"10.1016\/j.jag.2023.103309_b0085","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.apenergy.2016.08.191","article-title":"Automatic detection of solar photovoltaic arrays in high resolution aerial imagery","volume":"183","author":"Malof","year":"2016","journal-title":"Appl. Energy"},{"key":"10.1016\/j.jag.2023.103309_b0090","doi-asserted-by":"crossref","DOI":"10.1038\/s41597-022-01499-9","article-title":"An artificial intelligence dataset for solar energy locations in India","volume":"9","author":"Ortiz","year":"2022","journal-title":"Sci. Data"},{"key":"10.1016\/j.jag.2023.103309_b0095","first-page":"7471","article-title":"BASNet: Boundary-Aware Salient Object Detection","author":"Qin","year":"2019","journal-title":"IEEE Conf. Comput. Vis. Patt. Recogn. (CVPR)"},{"key":"10.1016\/j.jag.2023.103309_b0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2022.119876","article-title":"Automated extraction of energy systems information from remotely sensed data: A review and analysis","volume":"326","author":"Ren","year":"2022","journal-title":"Appl. Energy"},{"issue":"3","key":"10.1016\/j.jag.2023.103309_b0105","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","volume":"3","author":"Ronneberger","year":"2015","journal-title":"Int. Conf. Med. Image Comput. Comput.-Assisted Intervention."},{"key":"10.1016\/j.jag.2023.103309_b0110","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1038\/s41893-019-0352-9","article-title":"Six transformations to achieve the sustainable development goals","volume":"2","author":"Sachs","year":"2019","journal-title":"Nat. Sustain."},{"key":"10.1016\/j.jag.2023.103309_b0115","series-title":"Climate Change 2022: Mitigation of Climate Change","author":"Skea","year":"2022"},{"key":"10.1016\/j.jag.2023.103309_b0120","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-022-33976-5","article-title":"Energy requirements and carbon emissions for a low-carbon energy transition","volume":"13","author":"Slamer\u0161ak","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.jag.2023.103309_b0125","doi-asserted-by":"crossref","DOI":"10.1038\/s41597-020-00739-0","article-title":"A harmonised, high-coverage, open dataset of solar photovoltaic installations in the UK","volume":"7","author":"Stowell","year":"2020","journal-title":"Sci. Data"},{"key":"10.1016\/j.jag.2023.103309_b0130","doi-asserted-by":"crossref","unstructured":"Sun K, Xiao B, Liu D, Wang J.D., 2019. Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5693-5703.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"10.1016\/j.jag.2023.103309_b0135","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2020.143528","article-title":"Environmental impacts of solar photovoltaic systems: A critical review of recent progress and future outlook","volume":"759","author":"Tawalbeh","year":"2021","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.jag.2023.103309_b0140","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.isprsjprs.2018.04.010","article-title":"Photovoltaic panel extraction from very high-resolution aerial imagery using region-line primitive association analysis and template matching","volume":"141","author":"Wang","year":"2018","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"10.1016\/j.jag.2023.103309_b0145","doi-asserted-by":"crossref","first-page":"4117","DOI":"10.1016\/j.egyr.2022.03.039","article-title":"Mapping the rapid development of photovoltaic power stations in northwestern China using remote sensing","volume":"8","author":"Xia","year":"2022","journal-title":"Energy Rep."},{"key":"10.1016\/j.jag.2023.103309_b0150","article-title":"High-resolution mapping of water photovoltaic development in China through satellite imagery","volume":"107","author":"Xia","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.jag.2023.103309_b0155","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Adv. Neural inf. Proces. Syst."},{"key":"10.1016\/j.jag.2023.103309_b0160","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rser.2017.01.098","article-title":"Photovoltaic agriculture-new opportunity for photovoltaic applications in China","volume":"73","author":"Xue","year":"2017","journal-title":"Renew. Sust. Energ. Rev."},{"key":"10.1016\/j.jag.2023.103309_b0165","doi-asserted-by":"crossref","first-page":"2605","DOI":"10.1016\/j.joule.2018.11.021","article-title":"DeepSolar: A machine learning framework to efficiently construct a solar deployment database in the United States","volume":"2","author":"Yu","year":"2018","journal-title":"Joule"},{"key":"10.1016\/j.jag.2023.103309_b0170","doi-asserted-by":"crossref","first-page":"3743","DOI":"10.5194\/essd-14-3743-2022","article-title":"Mapping photovoltaic power plants in China using Landsat, random forest, and Google Earth Engine","volume":"14","author":"Zhang","year":"2022","journal-title":"Earth Syst. Sci. Data"},{"key":"10.1016\/j.jag.2023.103309_b0175","article-title":"Deep solar PV refiner: A detail-oriented deep learning network for refined segmentation of photovoltaic areas from satellite imagery","volume":"116","author":"Zhu","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.jag.2023.103309_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2020.106283","article-title":"The automatic segmentation of residential solar panels based on satellite images: A cross learning driven U-Net method","volume":"92","author":"Zhuang","year":"2020","journal-title":"Appl. Soft. Comput."}],"container-title":["International Journal of Applied Earth Observation and Geoinformation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1569843223001310?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1569843223001310?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T21:57:30Z","timestamp":1761602250000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1569843223001310"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5]]},"references-count":36,"alternative-id":["S1569843223001310"],"URL":"https:\/\/doi.org\/10.1016\/j.jag.2023.103309","relation":{},"ISSN":["1569-8432"],"issn-type":[{"value":"1569-8432","type":"print"}],"subject":[],"published":{"date-parts":[[2023,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"PVNet: A novel semantic segmentation model for extracting high-quality photovoltaic panels in large-scale systems from high-resolution remote sensing imagery","name":"articletitle","label":"Article Title"},{"value":"International Journal of Applied Earth Observation and Geoinformation","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jag.2023.103309","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"103309"}}