{"data":{"id":"10.6084/m9.figshare.c.3290519","type":"dois","attributes":{"doi":"10.6084/m9.figshare.c.3290519","prefix":"10.6084","suffix":"m9.figshare.c.3290519","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Bradbury, Kyle","nameType":"Personal","givenName":"Kyle","familyName":"Bradbury","affiliation":[],"nameIdentifiers":[{"nameIdentifier":"https://orcid.org/0000-0001-9847-0243","nameIdentifierScheme":"ORCID"}]},{"name":"Brigman, Benjamin","nameType":"Personal","givenName":"Benjamin","familyName":"Brigman","affiliation":[],"nameIdentifiers":[{"nameIdentifier":"https://orcid.org/0000-0001-7674-2439","nameIdentifierScheme":"ORCID"}]},{"name":"Collins, Leslie","nameType":"Personal","givenName":"Leslie","familyName":"Collins","affiliation":[],"nameIdentifiers":[]},{"name":"Johnson, Timothy","nameType":"Personal","givenName":"Timothy","familyName":"Johnson","affiliation":[],"nameIdentifiers":[]},{"name":"Lin, Sebastian","nameType":"Personal","givenName":"Sebastian","familyName":"Lin","affiliation":[],"nameIdentifiers":[]},{"name":"Newell, Richard","nameType":"Personal","givenName":"Richard","familyName":"Newell","affiliation":[],"nameIdentifiers":[{"nameIdentifier":"https://orcid.org/0000-0002-3205-5562","nameIdentifierScheme":"ORCID"}]},{"name":"Park, Sophia","nameType":"Personal","givenName":"Sophia","familyName":"Park","affiliation":[],"nameIdentifiers":[]},{"name":"Sunith Suresh","affiliation":[],"nameIdentifiers":[]},{"name":"Hoel Wiesner","affiliation":[],"nameIdentifiers":[]},{"name":"Xi, Yue","nameType":"Personal","givenName":"Yue","familyName":"Xi","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Aerial imagery object identification dataset for building and road detection, and building height estimation"}],"publisher":"Figshare","container":{},"publicationYear":2016,"subjects":[{"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":"80106 Image Processing","subjectScheme":"FOR"},{"subject":"80109 Pattern Recognition and Data Mining","subjectScheme":"FOR"},{"subject":"129999 Built Environment and Design not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Civil engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Civil engineering","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2016-07-28","dateType":"Created"},{"date":"2016-07-29","dateType":"Updated"},{"date":"2016","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"Collection","resourceType":"Collection","resourceTypeGeneral":"Collection"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"CC-BY","rightsUri":"http://creativecommons.org/licenses/by/3.0/us"}],"descriptions":[{"description":"Automated object detection in high-resolution aerial imagery\ncan provide valuable information in fields ranging from urban planning and\noperations to economic research, however, automating the process of analyzing\naerial imagery requires training data for machine learning algorithm\ndevelopment. This dataset seeks to meet that need. For 25 locations across 9\nU.S. cities, this dataset provides (1) high resolution aerial imagery; (2) annotations\nof over 40,000 building footprints (OSM shapefiles) as well as road polylines;\nand (3) topographical height data (LIDAR). This dataset can be used as ground\ntruth to train computer vision and machine learning algorithms for object\nidentification and analysis, in particular for building detection and height\nestimation, as well as road detection. \n\n\u003cbr\u003eA complete description of the data can be found in the pdf\nfile within the metadata for this collection, titled, “Metadata - building\n(area and height) and road dataset”. This data collection is organized such\nthat there is a separate dataset in this collection for each of the 9 cities\nwith available data (Arlington, MA; Atlanta, GA; Austin TX; Washington, DC; New\nHaven, CT; New York City, NY; Norfolk, VA; San Francisco, CA; Seekonk, MA). A map\nof these cities and example images can be found here: http://arcg.is/2afcSOk.\n\n\u003cbr\u003eImagery data from the United States Geological Survey (USGS);\nbuilding and road shapefiles are from OpenStreetMaps (OSM) (these OSM data are\nmade available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/);\nand the Lidar data are from U.S. National Oceanic and Atmospheric\nAdministration (NOAA), the Texas Natural Resources Information System (TNRIS).","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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