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http://dx.doi.org/10.22640/lxsiri.2020.50.2.37

Object Classification and Change Detection in Point Clouds Using Deep Learning  

Seo, Hong-Deok (Department of Spatial Information Engineering, Namseoul University)
Kim, Eui-Myoung (Department of Spatial Information Engineering, Namseoul University)
Publication Information
Journal of Cadastre & Land InformatiX / v.50, no.2, 2020 , pp. 37-51 More about this Journal
Abstract
With the development of machine learning and deep learning technologies, there has been increasing interest and attempt to apply these technologies to the detection of urban changes. However, the traditional methods of detecting changes and constructing spatial information are still often performed manually by humans, which is costly and time-consuming. Besides, a large number of people are needed to efficiently detect changes in buildings in urban areas. Therefore, in this study, a methodology that can detect changes by classifying road, building, and vegetation objects that are highly utilized in the geospatial information field was proposed by applying deep learning technology to point clouds. As a result of the experiment, roads, buildings, and vegetation were classified with an accuracy of 92% or more, and attributes information of the objects could be automatically constructed through this. In addition, if time-series data is constructed, it is thought that changes can be detected and attributes of existing digital maps can be inspected through the proposed methodology.
Keywords
Artificial Intelligence; Deep Learning; Point Cloud; Spatial Information; Change Detection;
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Times Cited By KSCI : 14  (Citation Analysis)
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