公制(单位)
计算机科学
激光雷达
树(集合论)
领域(数学)
城市规划
封面(代数)
遥感
代表(政治)
特征(语言学)
地理
土地覆盖
地图学
计算机视觉
土地利用
数学
生态学
机械工程
数学分析
语言学
运营管理
哲学
政治
政治学
法学
纯数学
工程类
经济
生物
作者
Ian Seiferling,Nikhil Naik,Carlo Ratti,Raphaël Proulx
标识
DOI:10.1016/j.landurbplan.2017.05.010
摘要
Traditional tools to map the distribution of urban green space have been hindered by either high cost and labour inputs or poor spatial resolution given the complex spatial structure of urban landscapes. What’s more, those tools do not observe the urban landscape from a perspective in which citizens experience a city. We test a novel application of computer vision to quantify urban tree cover at the street-level. We do so by utilizing the open-source image data of city streetscapes that is now abundant (Google Street View). We show that a multi-step computer vision algorithm segments and quantifies the percent of tree cover in streetscape images to a high degree of precision. By then modelling the relationship between neighbouring images along city street segments, we are able to extend this image representation and estimate the amount of perceived tree cover in city streetscapes to a relatively high level of accuracy for an entire city. Though not a replacement for high resolution remote sensing (e.g., aerial LiDAR) or intensive field surveys, the method provides a new multi-feature metric of urban tree cover that quantifies tree presence and distribution from the same viewpoint in which citizens experience and see the urban landscape.
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