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http://dx.doi.org/10.11108/kagis.2021.24.3.083

Classification of Urban Green Space Using Airborne LiDAR and RGB Ortho Imagery Based on Deep Learning  

SON, Bokyung (Dept. of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
LEE, Yeonsu (Dept. of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
IM, Jungho (Dept. of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Publication Information
Journal of the Korean Association of Geographic Information Studies / v.24, no.3, 2021 , pp. 83-98 More about this Journal
Abstract
Urban green space is an important component for enhancing urban ecosystem health. Thus, identifying the spatial structure of urban green space is required to manage a healthy urban ecosystem. The Ministry of Environment has provided the level 3 land cover map(the highest (1m) spatial resolution map) with a total of 41 classes since 2010. However, specific urban green information such as street trees was identified just as grassland or even not classified them as a vegetated area in the map. Therefore, this study classified detailed urban green information(i.e., tree, shrub, and grass), not included in the existing level 3 land cover map, using two types of high-resolution(<1m) remote sensing data(i.e., airborne LiDAR and RGB ortho imagery) in Suwon, South Korea. U-Net, one of image segmentation deep learning approaches, was adopted to classify detailed urban green space. A total of three classification models(i.e., LRGB10, LRGB5, and RGB5) were proposed depending on the target number of classes and the types of input data. The average overall accuracies for test sites were 83.40% (LRGB10), 89.44%(LRGB5), and 74.76%(RGB5). Among three models, LRGB5, which uses both airborne LiDAR and RGB ortho imagery with 5 target classes(i.e., tree, shrub, grass, building, and the others), resulted in the best performance. The area ratio of total urban green space(based on trees, shrub, and grass information) for the entire Suwon was 45.61%(LRGB10), 43.47%(LRGB5), and 44.22%(RGB5). All models were able to provide additional 13.40% of urban tree information on average when compared to the existing level 3 land cover map. Moreover, these urban green classification results are expected to be utilized in various urban green studies or decision making processes, as it provides detailed information on urban green space.
Keywords
Urban green space; Image classification; Airborne LiDAR; RGB ortho imagery; Land cover map; U-Net;
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