Korean J. Remote Sens. 2023; 39(5): 885-890
Published online: October 31, 2023
https://doi.org/10.7780/kjrs.2023.39.5.3.1
© Korean Society of Remote Sensing
박선영 1)·송아람 2)·이양원 3)·임정호 4)*
1) 서울과학기술대학교 인공지능응용학과 조교수(Assistant Professor, Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea) 2) 경북대학교 위치정보시스템학과 조교수(Assistant Professor, Department of Location-Based Information System, Kyungpook National University, Sangju, Republic of Korea) 3) 부경대학교 지구환경시스템과학부 공간정보시스템공학전공 교수(Professor, Department of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University, Busan, Republic of Korea) 4) 울산과학기술원 지구환경도시건설공학과 교수(Professor, Department of Civil Urban Earth and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea)
As satellite technology progresses, a growing number of satellites—like CubeSat and radar satellites—are available with a higher spectral and spatial resolutions than previous. National initiatives used to be the main force behind satellite development, but current trends indicate that private enterprises are also actively exploring and developing new satellite technologies. This special issue examines the recent research results and advanced technology in remote sensing approaches for Earth environment analysis. These results provide important information for the development of satellite sensors in the future and are of great interest to researchers working with artificial intelligence in this field. The special issue introduces the latest advances in remote sensing technology and highlights studies that make use of data to monitor and forecast Earth’s environment. The objective is to provide direction for the future of remote sensing research.
Keywords Remote sensing, Satellite, Satellite application, Deep learning, Artificial intelligence
Korean J. Remote Sens. 2023; 39(5): 885-890
Published online October 31, 2023 https://doi.org/10.7780/kjrs.2023.39.5.3.1
Copyright © Korean Society of Remote Sensing.
박선영 1)·송아람 2)·이양원 3)·임정호 4)*
1) 서울과학기술대학교 인공지능응용학과 조교수(Assistant Professor, Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea) 2) 경북대학교 위치정보시스템학과 조교수(Assistant Professor, Department of Location-Based Information System, Kyungpook National University, Sangju, Republic of Korea) 3) 부경대학교 지구환경시스템과학부 공간정보시스템공학전공 교수(Professor, Department of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University, Busan, Republic of Korea) 4) 울산과학기술원 지구환경도시건설공학과 교수(Professor, Department of Civil Urban Earth and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea)
박선영 1)·송아람 2)·이양원 3)·임정호 4)*
As satellite technology progresses, a growing number of satellites—like CubeSat and radar satellites—are available with a higher spectral and spatial resolutions than previous. National initiatives used to be the main force behind satellite development, but current trends indicate that private enterprises are also actively exploring and developing new satellite technologies. This special issue examines the recent research results and advanced technology in remote sensing approaches for Earth environment analysis. These results provide important information for the development of satellite sensors in the future and are of great interest to researchers working with artificial intelligence in this field. The special issue introduces the latest advances in remote sensing technology and highlights studies that make use of data to monitor and forecast Earth’s environment. The objective is to provide direction for the future of remote sensing research.
Keywords: Remote sensing, Satellite, Satellite application, Deep learning, Artificial intelligence
Rogelio Ruzcko Tobias, Sejeong Bae, Hwanhee Cho, Jungho Im
Korean J. Remote Sens. 2024; 40(6): 1505-1521Jihyun Lee, Taeyeon Won, Kwangseob Kim, Jinwoo Kim, Seungchul Lee
Korean J. Remote Sens. 2024; 40(6): 1219-1227