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Im, Jungho (임정호)

Department
Department of Urban and Environmental Engineering(도시환경공학과)
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
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, JunghoARTICLE21 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 alARTICLE43 Comparative Assessment of Linear Regression and Machine Learning for Analyzing the Spatial Distribution of Ground-level NO2 Concentrations: A Case Study for Seoul, Korea
2021-12Estimation of Leaf Area Index Based on Machine Learning/PROSAIL Using Optical Satellite ImageryLee, Jaese; Kang, Yoojin; Son, Bokyung, et alARTICLE48 Estimation of Leaf Area Index Based on Machine Learning/PROSAIL Using Optical Satellite Imagery
2021-11Estimation of surface-level NO2 and O-3 concentrations using TROPOMI data and machine learning over East AsiaKang, Yoojin; Choi, Hyunyoung; Im, Jungho, et alARTICLE36 Estimation of surface-level NO2 and O-3 concentrations using TROPOMI data and machine learning over East Asia
2021-10Detection of Forest Fire Damage from Sentinel-1 SAR Data through the Synergistic Use of Principal Component Analysis and K-means Clustering [Sentinel-1 SAR K-means Clustering]Lee, Jaese; Kim, Woohyeok; Im, Jungho, et alARTICLE19 Detection of Forest Fire Damage from Sentinel-1 SAR Data through the Synergistic Use of Principal Component Analysis and K-means Clustering [Sentinel-1 SAR K-means Clustering]
2021-10Detection of arctic summer melt ponds using icesat-2 altimetry dataHan, Daehyeon; Kim, Young Jun; Jung, Sihun, et alARTICLE19 Detection of arctic summer melt ponds using icesat-2 altimetry data
2021-10Spatial downscaling of ocean colour-climate change initiative (OC-CCI) Forel-Ule Index using GOCI satellite image and machine learning techniqueSung, Taejun; Kim, Young Jun; Choi, Hyunyoung, et alARTICLE22 Spatial downscaling of ocean colour-climate change initiative (OC-CCI) Forel-Ule Index using GOCI satellite image and machine learning technique
2021-10One year of GOCI-II launch present and futureChoi, Jong-kuk; Park, Myung-sook; Han, Kyung-soo, et alARTICLE13 One year of GOCI-II launch present and future
2021-10Sensitivity analysis for CAS500-4 atmospheric correction using simulated images and suggestion of the use of geostationary satellite-based atmospheric parametersKang, Yoojin; Cho, Dongjin; Han, Daehyeon, et alARTICLE17 Sensitivity analysis for CAS500-4 atmospheric correction using simulated images and suggestion of the use of geostationary satellite-based atmospheric parameters
2021-10Disaster assessment, monitoring, and prediction using remote sensing and gisJung, Minyoung; Kim, Duk-jin; Sohn, Hong-Gyoo, et alARTICLE8 Disaster assessment, monitoring, and prediction using remote sensing and gis
2021-09항공 LiDAR 및 RGB 정사 영상을 이용한 딥러닝 기반의 도시녹지 분류손보경; 이연수; 임정호ARTICLE38 항공 LiDAR 및 RGB 정사 영상을 이용한 딥러닝 기반의 도시녹지 분류
2021-09Aerosol data assimilation and forecast using Geostationary Ocean Color Imager aerosol optical depth and in-situ observations during the KORUS-AQ observing periodKim, Ganghan; Lee, Seunghee; Im, Jungho, et alARTICLE59 Aerosol data assimilation and forecast using Geostationary Ocean Color Imager aerosol optical depth and in-situ observations during the KORUS-AQ observing period
2021-08Pre-trained feature aggregated deep learning-based monitoring of overshooting tops using multi-spectral channels of GeoKompsat-2A advanced meteorological imageryLee, Juhyun; Kim, Miae; Im, Jungho, et alARTICLE41 Pre-trained feature aggregated deep learning-based monitoring of overshooting tops using multi-spectral channels of GeoKompsat-2A advanced meteorological imagery
2021-08Estimation of Spatially Continuous Near-Surface Relative Humidity Over Japan and South KoreaPark, Haemi; Lee, Junghee; Yoo, Cheolhee, et alARTICLE74 Estimation of Spatially Continuous Near-Surface Relative Humidity Over Japan and South Korea
2021-08Satellite-based Drought Forecasting: Research Trends, Challenges, and Future DirectionsSon, Bokyung; Im, Jungho; Park, Sumin, et alARTICLE9 Satellite-based Drought Forecasting: Research Trends, Challenges, and Future Directions
2021-04Estimation of TROPOMI-derived Ground-level SO2 Concentrations Using Machine Learning Over East AsiaChoi, Hyunyoung; Kang, Yoojin; Im, JunghoARTICLE241 Estimation of TROPOMI-derived Ground-level SO2 Concentrations Using Machine Learning Over East Asia
2021-04Estimation of ground-level PM10 and PM2.5 concentrations using boosting-based machine learning from satellite and numerical weather prediction dataPark, Seohui; Kim, Miae; Im, JunghoARTICLE239 Estimation of ground-level PM10 and PM2.5 concentrations using boosting-based machine learning from satellite and numerical weather prediction data
2021-03On the use of machine learning based ensemble approaches to improve evapotranspiration estimates from croplands across a wide environmental gradientBai, Yun; Zhang, Sha; Bhattarai, Nishan, et alARTICLE88 On the use of machine learning based ensemble approaches to improve evapotranspiration estimates from croplands across a wide environmental gradient
2021-01A new drought monitoring approach: Vector Projection Analysis (VPA)Son, Bokyung; Park, Sumin; Im, Jungho, et alARTICLE103 A new drought monitoring approach: Vector Projection Analysis (VPA)
2021-01Improvement of SMAP sea surface salinity in river-dominated oceans using machine learning approachesJang, Eunna; Kim, Young Jun; Im, Jungho, et alARTICLE76 Improvement of SMAP sea surface salinity in river-dominated oceans using machine learning approaches

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