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A novel approach to measuring urban open space and eye-level greenness using supervised learning on panoramic imagery in Boston, MA

Authors: Kyle M Monahan*, Research Technology, Tufts Technology Services, Tufts University, Medford MA 02155, Kai Ying Lau, Sasaki Associates, Inc. Boston, MA, 02144, Judy Fung, Environmental Health & Engineering, Inc. 180 Wells Avenue, Suite 200 Newton, MA 02459, Aurora (Yuehui) Li, Urban and Environmental Policy and Planning, Tufts University, Medford MA 02144, Annie Nyugen, Research Technology, Tufts Technology Services, Tufts University, Medford MA 02155, Carolyn Talmadge, Research Technology, Tufts Technology Services, Tufts University, Medford MA 02155
Topics: Urban and Regional Planning, Spatial Analysis & Modeling, Human-Environment Geography
Keywords: sky view factor, street view panoramas, green space, eye-level greenness
Session Type: Poster
Day: 4/9/2020
Start / End Time: 11:10 AM / 12:25 PM
Room: Virtual Track 2
Presentation File: No File Uploaded


Street view panoramas have become increasingly available for urban areas in the United States, China, and other countries. Providers include Google Street View (GSV) and Baidu Street View (BSV), all of which provide hemispherical (fisheye) panoramic photos of the near-road environment. In this work, we apply an algorithm for estimating the sky view factor (SVF) from these panoramas, adding an assessment of eye-level greenness. The SVF was estimated for the Greater Boston area and compared to a LiDAR-derived digital surface model (DSM) provided by MassGIS. The eye-level greenness was estimated and compared to green space data from MassGIS. Spatial correlations between the SVF and the LiDAR-dervied DSM and correlations between eye-level greenness and green space estimates are being performed. Future work should investigate the interaction effects between green space and SVF, and provide these computational workflows as a user-friendly ArcMap toolbox/PyQGIS script or Python package.

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