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http://dx.doi.org/10.7848/ksgpc.2021.39.6.437

Analysis of Deep Learning Research Trends Applied to Remote Sensing through Paper Review of Korean Domestic Journals  

Lee, Changhui (Dept. of Civil Engineering, Seoul National University of Science and Technology)
Yun, Yerin (Dept. of Civil Engineering, Seoul National University of Science and Technology)
Bae, Saejung (School of Civil Engineering, Seoul National University of Science and Technology)
Eo, Yang Dam (Dept. of Civil and Environmental Engineering, Konkuk University)
Kim, Changjae (Dept. of Civil and Environmental Engineering, Myongji University)
Shin, Sangho (Geographic Information Division, National Geographic Information Institute, Ministry of Land, Infrastructure and Transport)
Park, Soyoung (Geographic Information Division, National Geographic Information Institute, Ministry of Land, Infrastructure and Transport)
Han, Youkyung (Dept. of Civil Engineering, Seoul National University of Science and Technology)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.39, no.6, 2021 , pp. 437-456 More about this Journal
Abstract
In the field of remote sensing in Korea, starting in 2017, deep learning has begun to show efficient research results compared to existing research methods. Currently, research is being conducted to apply deep learning in almost all fields of remote sensing, from image preprocessing to applications. To analyze the research trend of deep learning applied to the remote sensing field, Korean domestic journal papers, published until October 2021, related to deep learning applied to the remote sensing field were collected. Based on the collected 60 papers, research trend analysis was performed while focusing on deep learning network purpose, remote sensing application field, and remote sensing image acquisition platform. In addition, open source data that can be effectively used to build training data for performing deep learning were summarized in the paper. Through this study, we presented the problems that need to be solved in order for deep learning to be established in the remote sensing field. Moreover, we intended to provide help in finding research directions for researchers to apply deep learning technology into the remote sensing field in the future.
Keywords
Remote Sensing; Deep Learning; Analysis of Research Trend; Image Acquisition Platform; Open Source Data;
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