{"data":{"id":"10.6084/m9.figshare.6931088.v1","type":"dois","attributes":{"doi":"10.6084/m9.figshare.6931088.v1","prefix":"10.6084","suffix":"m9.figshare.6931088.v1","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":"Han, Qiwei","nameType":"Personal","givenName":"Qiwei","familyName":"Han","affiliation":[],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8384-3107","nameIdentifierScheme":"ORCID"}]},{"name":"Nair, Varun","nameType":"Personal","givenName":"Varun","familyName":"Nair","affiliation":[],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-0240-651X","nameIdentifierScheme":"ORCID"}]},{"name":"Pathirathna, Tamasha","nameType":"Personal","givenName":"Tamasha","familyName":"Pathirathna","affiliation":[],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-4096-8089","nameIdentifierScheme":"ORCID"}]},{"name":"You, Xiaolan","nameType":"Personal","givenName":"Xiaolan","familyName":"You","affiliation":[],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7103-8853","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Electric Transmission and Distribution Infrastructure Imagery Dataset"}],"publisher":"figshare","container":{},"publicationYear":2018,"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":"90607 Power and Energy Systems Engineering (excl. Renewable Power)","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":"90608 Renewable Power and Energy Systems Engineering (excl. Solar Cells)","subjectScheme":"FOR"},{"subject":"80104 Computer Vision","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":"170203 Knowledge Representation and Machine Learning","subjectScheme":"FOR"},{"subject":"FOS: Psychology","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Psychology","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"80106 Image Processing","subjectScheme":"FOR"},{"subject":"Artificial Intelligence and Image Processing"}],"contributors":[],"dates":[{"date":"2018-08-03","dateType":"Created"},{"date":"2018-08-03","dateType":"Updated"},{"date":"2018","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsIdenticalTo","relatedIdentifier":"10.6084/m9.figshare.6931088","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["28099708488 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"\u003cb\u003eOverview\u003c/b\u003eThe dataset contains fully annotated electric transmission and distribution infrastructure for approximately 321 sq km of high resolution satellite and aerial imagery from around the world. The imagery and associated infrastructure annotations span 14 cities and 5 continents, and were selected to represent diversity in human settlement density (i.e. rural vs urban), terrain type, and development index. This dataset may be of particular interest to those looking to train machine learning algorithms to automatically identify energy infrastructure in satellite imagery or for those working on domain adaptation for computer vision. Automated algorithms for identifying electricity infrastructure in satellite imagery may assist policy makers identify the best pathway to electrification for unelectrified areas.\u003cbr\u003e\u003cb\u003eData Sources\u003c/b\u003eThis dataset contains data sourced from the LINZ Data Service licensed for reuse under CC BY 4.0. \u003cbr\u003eThis dataset also contained extracts from the SpaceNet dataset:SpaceNet on Amazon Web Services (AWS). “Datasets.” The SpaceNet Catalog. Last modified April 30, 2018 (link below).\u003cbr\u003eOther imagery data included in this dataset are from the Connecticut Department of Energy and Environmental Protection and the U.S. Geological Survey. \u003cbr\u003eLinks to each of the imagery data sources are provided below as well as the link to the annotation tool and the github repository that provides tools for using these data.\u003cbr\u003e\u003cb\u003eAcknowledgements\u003c/b\u003eThis dataset was created as part of the Duke University Data+ project, \"Energy Infrastructure Map of the World\" (link below) in collaboration with the Information Initiative at Duke and the Duke University Energy Initiative.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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