TY - JOUR
T1 - Urban form and composition of street canyons
T2 - A human-centric big data and deep learning approach
AU - Middel, Ariane
AU - Lukasczyk, Jonas
AU - Zakrzewski, Sophie
AU - Arnold, Michael
AU - Maciejewski, Ross
N1 - Funding Information:
This research was sponsored by University of Kaiserslautern , grant “Microclimate Data Collection, Analysis, and Visualization” and supported by National Science Foundation (NSF) Award Number 1635490, “A Simulation Platform to Enhance Infrastructure and Community Resilience to Extreme Heat Events.” As well as NSF Award Number 1639227, “Flexible Model Compositions and Visual Representations for Planning and Policy Decisions for the Food-Energy-Water Nexus”. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsoring organizations.
Publisher Copyright:
© 2018 The Author(s)
PY - 2019/3
Y1 - 2019/3
N2 - Various research applications require detailed metrics to describe the form and composition of cities at fine scales, but the parameter computation remains a challenge due to limited data availability, quality, and processing capabilities. We developed an innovative big data approach to derive street-level morphology and urban feature composition as experienced by a pedestrian from Google Street View (GSV) imagery. We employed a scalable deep learning framework to segment 90-degree field of view GSV image cubes into six classes: sky, trees, buildings, impervious surfaces, pervious surfaces, and non-permanent objects. We increased the classification accuracy by differentiating between three view directions (lateral, down, and up) and by introducing a void class as training label. To model the urban environment as perceived by a pedestrian in a street canyon, we projected the segmented image cubes onto spheres and evaluated the fraction of each surface class on the sphere. To demonstrate the application of our approach, we analyzed the urban form and composition of Philadelphia County and three Philadelphia neighborhoods (suburb, center city, lower income neighborhood) using stacked area graphs. Our method is fully scalable to other geographic locations and constitutes an important step towards building a global morphological database to describe the form and composition of cities from a human-centric perspective.
AB - Various research applications require detailed metrics to describe the form and composition of cities at fine scales, but the parameter computation remains a challenge due to limited data availability, quality, and processing capabilities. We developed an innovative big data approach to derive street-level morphology and urban feature composition as experienced by a pedestrian from Google Street View (GSV) imagery. We employed a scalable deep learning framework to segment 90-degree field of view GSV image cubes into six classes: sky, trees, buildings, impervious surfaces, pervious surfaces, and non-permanent objects. We increased the classification accuracy by differentiating between three view directions (lateral, down, and up) and by introducing a void class as training label. To model the urban environment as perceived by a pedestrian in a street canyon, we projected the segmented image cubes onto spheres and evaluated the fraction of each surface class on the sphere. To demonstrate the application of our approach, we analyzed the urban form and composition of Philadelphia County and three Philadelphia neighborhoods (suburb, center city, lower income neighborhood) using stacked area graphs. Our method is fully scalable to other geographic locations and constitutes an important step towards building a global morphological database to describe the form and composition of cities from a human-centric perspective.
KW - Deep learning
KW - Google Street View
KW - Human-centric
KW - Spherical fractions
KW - Street canyon
KW - Urban form and composition
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U2 - 10.1016/j.landurbplan.2018.12.001
DO - 10.1016/j.landurbplan.2018.12.001
M3 - Article
AN - SCOPUS:85058227669
SN - 0169-2046
VL - 183
SP - 122
EP - 132
JO - Landscape and Urban Planning
JF - Landscape and Urban Planning
ER -