{"data":{"id":"10.6084/m9.figshare.3385780.v2","type":"dois","attributes":{"doi":"10.6084/m9.figshare.3385780.v2","prefix":"10.6084","suffix":"m9.figshare.3385780.v2","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Bradbury, Kyle","nameType":"Personal","givenName":"Kyle","familyName":"Bradbury","affiliation":[],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9847-0243","nameIdentifierScheme":"ORCID"}]},{"name":"Saboo, Raghav","nameType":"Personal","givenName":"Raghav","familyName":"Saboo","affiliation":[],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8175-360X","nameIdentifierScheme":"ORCID"}]},{"name":"Malof, Jordan","nameType":"Personal","givenName":"Jordan","familyName":"Malof","affiliation":[],"nameIdentifiers":[]},{"name":"Johnson, Timothy","nameType":"Personal","givenName":"Timothy","familyName":"Johnson","affiliation":[],"nameIdentifiers":[]},{"name":"Devarajan, Arjun","nameType":"Personal","givenName":"Arjun","familyName":"Devarajan","affiliation":[],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-7344-8824","nameIdentifierScheme":"ORCID"}]},{"name":"Zhang, Wuming","nameType":"Personal","givenName":"Wuming","familyName":"Zhang","affiliation":[],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-1605-0227","nameIdentifierScheme":"ORCID"}]},{"name":"Collins, Leslie","nameType":"Personal","givenName":"Leslie","familyName":"Collins","affiliation":[],"nameIdentifiers":[]},{"name":"Newell, Richard","nameType":"Personal","givenName":"Richard","familyName":"Newell","affiliation":[],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-3205-5562","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Distributed Solar Photovoltaic Array Location and Extent Data Set for Remote Sensing Object Identification"}],"publisher":"figshare","container":{},"publicationYear":2016,"subjects":[{"subject":"91305 Energy Generation, Conversion and Storage Engineering","subjectScheme":"FOR"},{"subject":"FOS: Mechanical engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Mechanical engineering","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"90608 Renewable Power and Energy Systems Engineering (excl. Solar Cells)","subjectScheme":"FOR"},{"subject":"FOS: Electrical engineering, electronic engineering, information engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Electrical engineering, electronic engineering, information engineering","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"80106 Image Processing","subjectScheme":"FOR"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"80109 Pattern Recognition and Data Mining","subjectScheme":"FOR"}],"contributors":[],"dates":[{"date":"2016-09-22","dateType":"Created"},{"date":"2020-07-30","dateType":"Updated"},{"date":"2016","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsPreviousVersionOf","relatedIdentifier":"10.6084/m9.figshare.3385780","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["131610547 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of our environment, anthropogenic systems, and natural resources. The components of energy systems that are visible from above may be assessed with these remote sensing data when combined with machine learning methods. Here we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited data on solar PV deployments at small geographic scales. We created a machine learning dataset to develop the process of automatically identifying solar PV locations through the use of remote sensing imagery.\u003cbr\u003eThis dataset contains the geospatial coordinates and border vertices for 19,863 solar panels across 601 high resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, and analysis of the socioeconomic correlates of PV deployment.\u003cbr\u003eLinks to the aerial photographs from Fresno, Stockton, Oxnard, and Modesto can be found in the 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