Journal ArticleDOI
Green streets − Quantifying and mapping urban trees with street-level imagery and computer vision
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In this article, a multi-step computer vision algorithm segments and quantifies the percent of tree cover in street-scapes images to a high degree of precision, and then models the relationship between neighbouring images along city street segments.About:
This article is published in Landscape and Urban Planning.The article was published on 2017-09-01. It has received 171 citations till now. The article focuses on the topics: Urban forestry & Tree (data structure).read more
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Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China.
TL;DR: Findings provide support that street view green and blue spaces are protective against depression for the elderly in China, yet longitudinal confirmation to infer causality is necessary.
Journal ArticleDOI
Measuring daily accessed street greenery: A human-scale approach for informing better urban planning practices
TL;DR: This analytical approach contributes to a growing trend in integrating large, freely-available datasets with machine learning to inform planners, and it makes a step forward for urban planning practices through focusing on the human-scale measurement of accessed street greenery.
Journal ArticleDOI
Street view imagery in urban analytics and GIS: A review
Filip Biljecki,Koichi Ito +1 more
TL;DR: In this article, a comprehensive systematic review of the state of the art of how street-level imagery is currently used in studies pertaining to the built environment is presented, showing that street view imagery is now clearly an entrenched component of urban analytics and GIScience.
Journal ArticleDOI
Impacts of Street-Visible Greenery on Housing Prices: Evidence from a Hedonic Price Model and a Massive Street View Image Dataset in Beijing
Yonglin Zhang,Rencai Dong +1 more
TL;DR: Computer vision is used to quantify the street-visible greenery and estimated the economic benefits that the neighbouring visible greenery would have on residential developments in Beijing and provides a scientific basis and reference for policy makers and city planners in road greening.
Journal ArticleDOI
Analyzing the effects of Green View Index of neighborhood streets on walking time using Google Street View and deep learning
Donghwan Ki,Sugie Lee +1 more
TL;DR: In this article, the authors examined the street Green View Index (GVI) and its associations with walking activities by different income groups using survey data on walking behaviors in 2350 residents in Seoul, Korea.
References
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Journal Article
R: A language and environment for statistical computing.
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Journal ArticleDOI
Efficient Graph-Based Image Segmentation
TL;DR: An efficient segmentation algorithm is developed based on a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image and it is shown that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties.
Journal Article
Completion of the 2006 National Land Cover Database for the conterminous United States.
Joyce Fry,George Xian,Suming Jin,Jon Dewitz,Collin G. Homer,Limin Yang,Christopher Barnes,Nathaniel D. Herold,James D. Wickham +8 more
Journal ArticleDOI
Citizen Science as an Ecological Research Tool: Challenges and Benefits
TL;DR: Citizen science, the involvement of volunteers in research, has increased the scale of ecological field studies with continent-wide, centralized monitoring efforts and tapping of volunteers to conduct large, coordinated, field experiments as mentioned in this paper.
Proceedings ArticleDOI
Geometric context from a single image
TL;DR: This work shows that it can estimate the coarse geometric properties of a scene by learning appearance-based models of geometric classes, even in cluttered natural scenes, and provides a multiple-hypothesis framework for robustly estimating scene structure from a single image and obtaining confidences for each geometric label.