Automatic Assessment of Green Space Ratio in Urban Areas from Mobile Scanning Data
AbstractIn this paper, we propose a method for using mobile laser-scanning data to estimate the green space ratio (GSR), a landscape index that represents the proportion of green area to the whole-view area. The proposed method first classifies and segments vegetation using voxel-based and shape-based approaches. Vertical planar-surface objects are excluded, and randomly distributed objects are extracted as vegetation via multi-spatial-scale analysis. Then, the method generates a map representing occlusion by vegetation, and estimates GSR at an arbitrary location. We applied the method to a data set collected in a residential area in Kyoto, Japan. We compared the results with the ground truth data and obtained a root mean squared error of approximately 4.1%. Although some non-vegetation with rough surfaces was falsely extracted as vegetation, our method seems to estimate GSR to an acceptable accuracy. View Full-Text
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Susaki, J.; Kubota, S. Automatic Assessment of Green Space Ratio in Urban Areas from Mobile Scanning Data. Remote Sens. 2017, 9, 215.
Susaki J, Kubota S. Automatic Assessment of Green Space Ratio in Urban Areas from Mobile Scanning Data. Remote Sensing. 2017; 9(3):215.Chicago/Turabian Style
Susaki, Junichi; Kubota, Seiya. 2017. "Automatic Assessment of Green Space Ratio in Urban Areas from Mobile Scanning Data." Remote Sens. 9, no. 3: 215.