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Im, Jungho (임정호)

Department
Department of Civil, Urban, Earth, and Environmental Engineering(지구환경도시건설공학과)
Website
http://iris.unist.ac.kr/
Lab
Intelligent Remote sensing and geospatial Information Science Lab. (환경원격탐사/인공지능 연구실)
Research Keywords
환경원격탐사, 인공지능, 공간모델링, 재난모니터링, 재난예측, Remote sensing, Geospatial modeling, Disaster monitoring and management, artificial intelligence
Research Interests
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.
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Issue DateTitleAuthor(s)TypeViewAltmetrics
2023-12Towards accurate individual tree parameters estimation in dense forest: optimized coarse-to-fine algorithms for registering UAV and terrestrial LiDAR dataZhao, Yuting; Im, Jungho; Zhen, Zhen, et alARTICLE1852 Towards accurate individual tree parameters estimation in dense forest: optimized coarse-to-fine algorithms for registering UAV and terrestrial LiDAR data
2023-12Precipitation nowcasting using ground radar data and simpler yet better video prediction deep learningHan, Daehyeon; Choo, Minki; Im, Jungho, et alARTICLE1266 Precipitation nowcasting using ground radar data and simpler yet better video prediction deep learning
2023-12Combination four different ensemble algorithms with the generalized linear model (GLM) for predicting forest fire susceptibilityJanizadeh, Saeid; Bateni, Sayed M.; Jun, Changhyun, et alARTICLE545 Combination four different ensemble algorithms with the generalized linear model (GLM) for predicting forest fire susceptibility
2023-12Toward an adaptable deep-learning model for satellite-based wildfire monitoring with consideration of environmental conditionsKang, Yoojin; Sung, Taejun; Im, JunghoARTICLE163 Toward an adaptable deep-learning model for satellite-based wildfire monitoring with consideration of environmental conditions
2023-09Direct aerosol optical depth retrievals using MODIS reflectance data and machine learning over East AsiaKang, Eunjin; Park, Seonyoung; Kim, Miae, et alARTICLE502 Direct aerosol optical depth retrievals using MODIS reflectance data and machine learning over East Asia
2023-09Novel Features of Canopy Height Distribution for Aboveground Biomass Estimation Using Machine Learning: A Case Study in Natural Secondary ForestsMa, Ye; Zhang, Lianjun; Im, Jungho, et alARTICLE282 Novel Features of Canopy Height Distribution for Aboveground Biomass Estimation Using Machine Learning: A Case Study in Natural Secondary Forests
2023-08A hybrid machine learning approach to investigate the changing urban thermal environment by dynamic land cover transformation: A case study of Suwon, republic of KoreaLee, Siwoo; Yoo, Cheolhee; Im, Jungho, et alARTICLE190 A hybrid machine learning approach to investigate the changing urban thermal environment by dynamic land cover transformation: A case study of Suwon, republic of Korea
2023-08Improved Ocean-Fog Monitoring Using Himawari-8 Geostationary Satellite Data Based on Machine Learning With SHAP-Based Model InterpretationSim, Seongmun; Im, JunghoARTICLE66 Improved Ocean-Fog Monitoring Using Himawari-8 Geostationary Satellite Data Based on Machine Learning With SHAP-Based Model Interpretation
2023-07Fine particulate concentrations over East Asia derived from aerosols measured by the advanced Himawari Imager using machine learningCho, Yeseul; Kim, Jhoon; Lee, Jeewoo, et alARTICLE192 Fine particulate concentrations over East Asia derived from aerosols measured by the advanced Himawari Imager using machine learning
2023-06Trend Analysis ofVegetation Changes of Korean Fir (Abies koreana Wilson) in Hallasan and Jirisan Using MODIS ImageryChoo, Minki; Yo, Cheolhee; Im, Jungho, et alARTICLE172 Trend Analysis ofVegetation Changes of Korean Fir (Abies koreana Wilson) in Hallasan and Jirisan Using MODIS Imagery
2023-05Atmospheric-correction-free red tide quantification algorithm for GOCI based on machine learning combined with a radiative transfer simulationKim, Young Jun; Kim, Wonkook; Im, Jungho, et alARTICLE191 Atmospheric-correction-free red tide quantification algorithm for GOCI based on machine learning combined with a radiative transfer simulation
2023-05Synergistic combination of information from ground observations, geostationary satellite, and air quality modeling towards improved PM2.5 predictabilityYu, Jinhyeok; Song, Chul H.; Lee, Dogyeong, et alARTICLE250 Synergistic combination of information from ground observations, geostationary satellite, and air quality modeling towards improved PM2.5 predictability
2023-04Retrieval of hourly PM2.5 using top-of-atmosphere reflectance from geostationary ocean color imagers I and IIChoi, Hyunyoung; Park, Seonyoung; Kang, Yoojin, et alARTICLE230 Retrieval of hourly PM2.5 using top-of-atmosphere reflectance from geostationary ocean color imagers I and II
2023-01Improved Tropical Cyclone Track Simulation over the Western North Pacific using the WRF Model and a Machine Learning MethodKim, Kyoungmin; Yoon, Donghyuck; Cha, Dong-Hyun, et alARTICLE507 Improved Tropical Cyclone Track Simulation over the Western North Pacific using the WRF Model and a Machine Learning Method
2023-01Remote sensing of sea surface salinity: challenges and research directionsKim, Young Jun; Han, Daehyeon; Jang, Eunna, et alARTICLE348 Remote sensing of sea surface salinity: challenges and research directions
2022-12Performance of Drought Indices in Assessing Rice Yield in North Korea and South Korea under the Different Agricultural SystemsPark, Seonyoung; Lee, Jaese; Yeom, Jongmin, et alARTICLE119 Performance of Drought Indices in Assessing Rice Yield in North Korea and South Korea under the Different Agricultural Systems
2022-11A deep learning model using geostationary satellite data for forest fire detection with reduced detection latencyKang, Yoojin; Jang, Eunna; Im, Jungho, et alARTICLE288 A deep learning model using geostationary satellite data for forest fire detection with reduced detection latency
2022-10An Advanced Operational Approach for Tropical Cyclone Center Estimation Using Geostationary-Satellite-Based Water Vapor and Infrared ChannelsShin, Yeji; Lee, Juhyun; Im, Jungho, et alARTICLE244 An Advanced Operational Approach for Tropical Cyclone Center Estimation Using Geostationary-Satellite-Based Water Vapor and Infrared Channels
2022-10Estimation of shrub willow biophysical parameters across time and space from Sentinel-2 and unmanned aerial system (UAS) dataXu, Jin; Quackenbush, Lindi J.; Volk, Timothy A., et alARTICLE265 Estimation of shrub willow biophysical parameters across time and space from Sentinel-2 and unmanned aerial system (UAS) data
2022-10Development of Mid-range Forecast Models of Forest Fire Risk Using Machine LearningPark, Sumin; Son, Bokyung; Im, Jungho, et alARTICLE541 Development of Mid-range Forecast Models of Forest Fire Risk Using Machine Learning

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