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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
2022-10Estimation of High Resolution Sea Surface Salinity Using Multi Satellite Data and Machine LearningSung, Taejun; Sim, Seongmun; Jang, Eunna, et alARTICLE584 Estimation of High Resolution Sea Surface Salinity Using Multi Satellite Data and Machine Learning
2022-10Forest Burned Area Detection Using Landsat 8/9 and Sentinel-2 A/B Imagery with Various Indices: A Case Study of UljinKim, Byeongcheol; Lee, Kyungil; Park, Seonyoung, et alARTICLE439 Forest Burned Area Detection Using Landsat 8/9 and Sentinel-2 A/B Imagery with Various Indices: A Case Study of Uljin
2022-10Disaster Prediction, Monitoring, and Response Using Remote Sensing and GISKim, Junwoo; Kim, Duk-jin; Sohn, Hong-Gyoo, et alARTICLE496 Disaster Prediction, Monitoring, and Response Using Remote Sensing and GIS
2022-10Generation of Daily High-resolution Sea Surface Temperature for the Seas around the Korean Peninsula Using Multi-satellite Data and Artificial IntelligenceJung, Sihun; Choo, Minki; Im, Jungho, et alARTICLE484 Generation of Daily High-resolution Sea Surface Temperature for the Seas around the Korean Peninsula Using Multi-satellite Data and Artificial Intelligence
2022-09Multi-Platform LiDAR for Non-Destructive Individual Aboveground Biomass Estimation for Changbai Larch (Larix olgensis Henry) Using a Hierarchical Bayesian ApproachWang, Man; Im, Jungho; Zhao, Yinghui, et alARTICLE335 Multi-Platform LiDAR for Non-Destructive Individual Aboveground Biomass Estimation for Changbai Larch (Larix olgensis Henry) Using a Hierarchical Bayesian Approach
2022-09스태킹 앙상블 모델을 이용한 시간별 지상 오존 공간내삽 정확도 향상김예진; 강은진; 조동진, et alARTICLE185 스태킹 앙상블 모델을 이용한 시간별 지상 오존 공간내삽 정확도 향상
2022-09우리나라 연안 대형저서동물 시·공간 군집 특성 분석김영준; 임정호; 조춘옥, et alARTICLE283 우리나라 연안 대형저서동물 시·공간 군집 특성 분석
2022-08Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast AsiaPark, Seohui; Im, Jungho; Kim, Jhoon, et alARTICLE306 Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia
2022-06Improved soil moisture estimation: Synergistic use of satellite observations and land surface models over CONUS based on machine learningLee, Jaese; Park, Sumin; Im, Jungho, et alARTICLE413 Improved soil moisture estimation: Synergistic use of satellite observations and land surface models over CONUS based on machine learning
2022-06Downscaling MODIS nighttime land surface temperatures in urban areas using ASTER thermal data through local linear forestYoo, Cheolhee; Im, Jungho; Cho, Dongjin, et alARTICLE303 Downscaling MODIS nighttime land surface temperatures in urban areas using ASTER thermal data through local linear forest
2022-05Global sea surface salinity via the synergistic use of SMAP satellite and HYCOM data based on machine learningJang, Eunna; Kim, Young Jun; Im, Jungho, et alARTICLE303 Global sea surface salinity via the synergistic use of SMAP satellite and HYCOM data based on machine learning
2022-04All-Sky 1 km MODIS Land Surface Temperature Reconstruction Considering Cloud Effects Based on Machine LearningCho, Dongjin; Bae, Dukwon; Yoo, Cheolhee, et alARTICLE423 All-Sky 1 km MODIS Land Surface Temperature Reconstruction Considering Cloud Effects Based on Machine Learning
2022-04Development of model output statistics based on the least absolute shrinkage and selection operator regression for forecasting next-day maximum temperature in South KoreaYoon, Donghyuck; Kim, Kyoungmin; Cha, Dong-Hyun, et alARTICLE454 Development of model output statistics based on the least absolute shrinkage and selection operator regression for forecasting next-day maximum temperature in South Korea
2022-03A novel ensemble learning for post-processing of NWP Model's next-day maximum air temperature forecast in summer using deep learning and statistical approachesCho, Dongjin; Yoo, Cheolhee; Son, Bokyung, et alARTICLE429 A novel ensemble learning for post-processing of NWP Model's next-day maximum air temperature forecast in summer using deep learning and statistical approaches
2022-02High-Resolution Seamless Daily Sea Surface Temperature Based on Satellite Data Fusion and Machine Learning over Kuroshio ExtensionJung, Sihun; Yoo, Cheolhee; Im, JunghoARTICLE388 High-Resolution Seamless Daily Sea Surface Temperature Based on Satellite Data Fusion and Machine Learning over Kuroshio Extension
2022-01Air Quality Forecasts Improved by Combining Data Assimilation and Machine Learning With Satellite AODLee, Seunghee; Park, Seohui; Lee, Myong-In, et alARTICLE713 Air Quality Forecasts Improved by Combining Data Assimilation and Machine Learning With Satellite AOD
2022-01A Novel Tropical Cyclone Size Estimation Model Based on a Convolutional Neural Network Using Geostationary Satellite ImageryBaek, You-Hyun; Moon, Il-Ju; Im, Jungho, et alARTICLE361 A Novel Tropical Cyclone Size Estimation Model Based on a Convolutional Neural Network Using Geostationary Satellite Imagery
2022-01Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East AsiaKang, Yoojin; Kim, Miae; Kang, Eunjin, et alARTICLE396 Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia
2021-12Rainfall intensity estimation using geostationary satellite data based on machine learning: A case study in the korean peninsula in summerShin, Yeji; Han, Daehyeon; Im, JunghoARTICLE814 Rainfall intensity estimation using geostationary satellite data based on machine learning: A case study in the korean peninsula in summer
2021-12Comparative Assessment of Linear Regression and Machine Learning for Analyzing the Spatial Distribution of Ground-level NO2 Concentrations: A Case Study for Seoul, KoreaKang, Eunjin; Yoo, Cheolhee; Shin, Yeji, et alARTICLE940 Comparative Assessment of Linear Regression and Machine Learning for Analyzing the Spatial Distribution of Ground-level NO2 Concentrations: A Case Study for Seoul, Korea

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