Faculty Research Profile
Jungho Im
학력
· 2006: Ph.D. in Geography, University of South Carolina
· 2000: M.C.P. in Environmental Management, Seoul National University
· 1998: B.S. in Oceanography, Seoul National University
주요 경력
· 2019⎼present: Full Professor, UNIST
· 2014⎼2019: Associate Professor, UNIST
· 2012⎼2014: Assistant Professor, UNIST
· 2007⎼2012: Assistant Professor, Environmental Resources Engineering, State University of New York, College of Environmental Science and Forestry
· 2006⎼2007: Research Associate, Center for GIS and Remote Sensing, University
수상/학회/외부활동
· 2014⎼present: Editor-in-Chief, GIScience and Remote Sensing
· 2020⎼present: Associate Editor, ISPRS Journal of Photogrammetry and Remote Sensing
환경원격탐사/인공지능 연구실
Intelligent Remote sensing and geospatial Information Science
IRIS 랩의 주 연구분야는 원격탐사 자료와 공간 정보, 인공지능을 활용하여 변화하는 기후 속에서 지구상에서 나타나는 다양한 현상들을 이해하고, 모니터링 및 예측 분석하여 지구 시스템을 보다 더 잘 이해할 수 있도록 하는데 초점을 두고 있다. 특히 최근 들어 많은 이슈가 되고 있는 폭염, 태풍(집중호우), 미세먼지, 가 뭄, 산불 등 재난재해 분야에 집중하고 있으며, 다양한 소스의 관측 및 모델자료와 최신 인공지능 기법을 융합하여 연구에 활용하고 있다.
The IRIS lab utilizes remote sensing, GIS modeling, and artificial intelligence techniques to broaden and deepen our understanding of the Earth science under climate variability/change, and leverages this knowledge to better manage and control critical functions related to terrestrial, coastal, and polar ecosystems, natural and man-made disasters, water resources, and carbon sequestration.
The IRIS lab utilizes remote sensing, GIS modeling, and artificial intelligence techniques to broaden and deepen our understanding of the Earth science under climate variability/change, and leverages this knowledge to better manage and control critical functions related to terrestrial, coastal, and polar ecosystems, natural and man-made disasters, water resources, and carbon sequestration.
연구분야
원격탐사, 인공 지능, 공간 모델링, 재난 모니터링 및 예측 / Remote sensing, artificial intelligence, spatial modeling, disaster monitoring and prediction
Remote sensing, artificial intelligence, spatial modeling, disaster monitoring and prediction
연구주제
· 원격탐사와 AI 융합 활용한 재난 모니터링 및 평가
Disaster Monitoring and Assessment using Remote Sensing and Artificial Intelligence
· 극지 원격 탐사
Polar Remote Sensing
· 대기질/기상/기후 원격탐사
Remote Sensing for Air Quality, Meteorology, and Climate
· 수자원 및 해양 원격탐사
Remote Sensing for Water Resources and Ocean
· Disaster Monitoring and Assessment using Remote Sensing and Artificial Intelligence
· Polar Remote Sensing
· Remote Sensing for Air Quality, Meteorology, and Climate
· Remote Sensing for Water Resources and Ocean
국가연구개발사업 기술 분류체계
국가과학기술표준분류
ND. 지구과학(지구/대기/해양/천문)
논문
· Lee, S., Yoo, C., Im, J.*, Cho, D., Lee, Y., Bae, D. (2023). A novel approach to investigating the changing urban thermal environment by dynamic land cover transformation: A case study of Suwon, Republic of Korea. International Journal of Applied Earth observation and Geoinformation, 122, 103408
· Choi, H., Park, S., Kang, Y., Im, J.*, Song, S. (2023). Retrieval of hourly PM2.5 using top-of-atmosphere reflectance from Geostationary Ocean Color Imagers I and II. Environmental Pollution, 323, 121169
· Jang, E., Kim, Y., Im, J.*, Park, Y., Sung, T. (2022). Global sea surface salinity generated through the synergistic use of SMAP satellite and HYCOM data based on machine learning approaches. Remote Sensing of Environment, 273, 112980
· Son, B., Park, S., Im, J.*, Park, S., Ke, Y., Quackenbush, L. (2021). A new drought monitoring approach: Vector-Projection Analysis(VPA). Remote Sensing of Environment, 252, 112145