​Parcel feature data derived from Google Street View images for urban land use classification in Brooklyn, New York Cityfor urban land use classification in Brooklyn, New York Cityretain-->

Weixing Zhang; Weidong Li; Chuanrong Zhang; Dean M Hanink; Xiaojiang Li; Wenjie Wang

doi:10.1016/j.dib.2017.04.002

Parcel feature data derived from Google Street View images for urban land use classification in Brooklyn, New York Cityfor urban land use classification in Brooklyn, New York Cityretain-->

Data Brief. 2017 Apr 8:12:175-179. doi: 10.1016/j.dib.2017.04.002. eCollection 2017 Jun.

Authors

Weixing Zhang^{1

2

3}, Weidong Li¹, Chuanrong Zhang^{1

2}, Dean M Hanink¹, Xiaojiang Li^{1

2}, Wenjie Wang^{1

2

3}

Affiliations

¹ Department of Geography, University of Connecticut, Storrs, CT 06269-4148, USA.
² Center for Environmental Science and Engineering, University of Connecticut, Storrs, CT 06269-4148, USA.
³ Connecticut State Data Center, University of Connecticut, Storrs, CT 06269-4148, USA.

Abstract

Google Street View (GSV) was used for urban land use classification, together with airborne light detection and ranging (LiDAR) data and high resolution orthoimagery, by a parcel-based method. In this data article, we present the input raw GSV images, intermediate products of GSV images, and final urban land use classification data that are related to our research article "Parcel-based urban land use classification in megacity using airborne LiDAR, high resolution orthoimagery, and Google Street View" (Zhang et al., 2017) [1]. More detail about other used data and our findings can be found in Zhang et al. (2017) [1].

Keywords: Detected text; Google Street View; Parcel feature; Urban land use classification.