​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 in Brief

Volumn 12, Issue , 2017, Pages 175-179

  Zhang, Weixing ^a   Li, Weidong ^a   Zhang, Chuanrong ^a   Hanink, Dean M ^a   Li, Xiaojiang ^a   Wang, Wenjie ^a  

^a UNIVERSITY OF CONNECTICUT   (United States)

Author keywords

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

Indexed keywords

CLASSIFICATION (OF INFORMATION); IMAGE CLASSIFICATION; LAND USE; TEXT PROCESSING;

BROOKLYN; DETECTED TEXT; GOOGLE STREET VIEW; GOOGLE+; HIGH RESOLUTION; LANDUSE CLASSIFICATIONS; LIGHT DETECTION AND RANGING; PARCEL FEATURE; URBAN LAND USE; URBAN LAND USE CLASSIFICATION;

OPTICAL RADAR;

EID: 85017530421     PISSN: None     EISSN: 23523409     Source Type: Journal    
DOI: 10.1016/j.dib.2017.04.002     Document Type: DP

References (4)

1. [1] Zhang, W., Li, W., Zhang, C., Hanink, D.M., Li, X., Wang, W., Parcel-based urban land use classification in megacity using airborne LiDAR, high resolution orthoimagery, and Google Street View. Comput. Environ. Urban Syst. 64 (2017), 215–228, 10.1016/j.compenvurbsys.2017.03.001.
2. [2] Wu, S., Silvánhyphen, J., Cárdenas, Wang, L., Per‐field urban land use classification based on tax parcel boundaries. Int. J. Remote Sens. 28:12 (2007), 2777–2801, 10.1080/01431160600981541.
3. [3] Wu, S., Qiu, X., Usery, E.L., Wang, L., Using geometrical, textural, and contextual information of land parcels for classification of detailed urban land use. Ann. Assoc. Am. Geogr. 99:1 (2009), 76–98, 10.1080/00045600802459028.
4. [4] Hu, S., Wang, L., Automated urban land-use classification with remote sensing. Int. J. Remote Sens. 34:3 (2013), 790–803, 10.1080/01431161.2012.714510.