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http://dx.doi.org/10.6109/jkiice.2020.24.3.349

Measurements of Green Space Ratio in Google Earth using Convolutional Neural Network  

Youn, Yeo-Su (Department of Computer Engineering, Cheongju University)
Kim, Kwang-Baek (Division of Computer Software Engineering, Silla University)
Park, Hyun-Jun (Division of Software Convergence, Cheongju University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.3, 2020 , pp. 349-354 More about this Journal
Abstract
The preliminary investigation to expand the green space requires a lot of cost and time. In this paper, we solve the problem by measuring the ratio of green space in a specific region through a convolutional neural network based the green space classification using Google Earth images. First, the proposed method collects various region images in Google Earth and learns them by using the convolutional neural network. The proposed method divides the image recursively to measure the green space ratio of the specific region, and it determines whether the divided image is green space using a trained convolutional neural network model, and then the green space ratio is calculated using the regions determined as the green space. Experimental results show that the proposed method shows high performance in measuring green space ratios in various regions.
Keywords
Green space; Convolutional neural network; Google Earth; Green space ratio;
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Times Cited By KSCI : 4  (Citation Analysis)
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