Parcel-based urban land use classification in megacity using airborne LiDAR, high resolution orthoimagery, and Google Street View.pdfVIP

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Parcel-based urban land use classification in megacity using airborne LiDAR, high resolution orthoimagery, and Google Street View.pdf

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Parcel-based urban land use classification in megacity using airborne LiDAR, high resolution orthoimagery, and Google Street View.pdf

Computers, Environment and Urban Systems 64 (2017) 215–228 Contents lists available at ScienceDirect Computers, Environment and Urban Systems journal homepage: /locate/ceus Parcel-based urban land use classi?cation in megacity using airborne LiDAR, high resolution orthoimagery, and Google Street View Weixing Zhang a,b,c, Weidong Li a, Chuanrong Zhang a,b,?, Dean M. Hanink a, Xiaojiang Li a,b, Wenjie Wang a,b,c a Department of Geography, University of Connecticut, Storrs, CT 06269-4148, USA b Center for Environmental Science and Engineering, University of Connecticut, Storrs, CT 06269-4148, USA c Connecticut State Data Center, University of Connecticut, Storrs, CT 06269-4148, USA article info Article history: Received 3 October 2016 Received in revised form 5 February 2017 Accepted 4 March 2017 Available online xxxx Keywords: Urban land use Classi?cation Megacity Google Street View LiDAR Parcel feature abstract Urban land use information is increasingly important for a variety of purposes. With their increasing coverage and availability, airborne light detection and ranging (LiDAR) data, high resolution orthoimagery (HRO), and Google Street View (GSV) images are showing great potential for accurate land use classi?cation. However, no study mapped land use in megacity using GSV-derived features or the three kinds of data together for land use classi?cation. The main objectives of this study are (1) to test the performance of a parcel-based land use classi?cation method using a Random Forest classi?er with LiDAR data, HRO, and GSV images in a megacity, and (2) to explore the use of GSV in separating parcels of mixed residential commercial buildings from other land use parcels. Two neighboring community districts in Brooklyn, New York, were selected as the study area. Thirteen automatically-derived parcel features, including nine common parcel features and four GSV-derived parcel features, were used in land use classi?cation. The average overall classi?cation accuracy