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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 Date	Title	Author(s)	Type	View	Altmetrics
2020-05	Estimation of All-Weather 1 km MODIS Land Surface Temperature for Humid Summer Days	Yoo, Cheolhee; Im, Jungho; Cho, Dongjin, et al	ARTICLE	362	Estimation of All-Weather 1 km MODIS Land Surface Temperature for Humid Summer Days
2020-04	Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models	Park, Seohui; Lee, Junghee; Im, Jungho, et al	ARTICLE	401	Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models
2020-04	Recent ENSO influence on East African drought during rainy seasons through the synergistic use of satellite and reanalysis data	Park, Seonyoung; Kang, Daehyun; Yoo, Cheolhee, et al	ARTICLE	380	Recent ENSO influence on East African drought during rainy seasons through the synergistic use of satellite and reanalysis data
2020-04	Comparative Assessment of Various Machine Learning-Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas	Cho, Dongjin; Yoo, Cheolhee; Im, Jungho, et al	ARTICLE	595	Comparative Assessment of Various Machine Learning-Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas
2020-04	Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery	Lee, Junghee; Han, Daehyeon; Shin, Minso, et al	ARTICLE	501	Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery
2020-03	Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks	Kim, Young Jun; Kim, Hyun-Cheol; Han, Daehyeon, et al	ARTICLE	563	Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks
2020-02	Estimating ground-level particulate matter concentrations using satellite-based data: a review	Shin, Minso; Kang, Yoojin; Park, Seohui, et al	ARTICLE	582	Estimating ground-level particulate matter concentrations using satellite-based data: a review
2020-01	Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data	Lee, Juhyun; Im, Jungho; Cha, Dong-Hyun, et al	ARTICLE	659	Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data
2019-12	기상 예보 및 위성 자료를 이용한 우리나라 산불위험지수의 시공간적 고도화	강유진; 박수민; 장은나, et al	ARTICLE	595	기상 예보 및 위성 자료를 이용한 우리나라 산불위험지수의 시공간적 고도화
2019-12	산불발생위험 추정을 위한 위성기반 가뭄지수 개발	박수민; 손보경; 임정호, et al	ARTICLE	369	산불발생위험 추정을 위한 위성기반 가뭄지수 개발
2019-11	Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images	Yoo, Cheolhee; Han, Daehyeon; Im, Jungho, et al	ARTICLE	513	Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images
2019-11	Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation	Liu, Maolin; Ke, Yinghai; Yin, Qi, et al	ARTICLE	364	Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation
2019-10	Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches	Park, Sumin; Park, Haemi; Im, Jungho, et al	ARTICLE	550	Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches
2019-08	Retrieval of total precipitable water from Himawari-8 AHI data: A comparison of random forest, extreme gradient boosting, and deep neural network	Lee, Yeonjin; Han, Daehyeon; Ahn, Myoung-Hwan, et al	ARTICLE	514	Retrieval of total precipitable water from Himawari-8 AHI data: A comparison of random forest, extreme gradient boosting, and deep neural network
2019-08	Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery	Li, Siqi; Quackenbush, Lindi J.; Im, Jungho	ARTICLE	549	Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery
2019-07	Zooplankton and micronekton respond to climate fluctuations in the Amundsen Sea polynya, Antarctica	La, Hyoung Sul; Park, Keyhong; Wahlin, Anna, et al	ARTICLE	638	Zooplankton and micronekton respond to climate fluctuations in the Amundsen Sea polynya, Antarctica
2019-06	Improvement of satellite-based estimation of gross primary production through optimization of meteorological parameters and high resolution land cover information at regional scale over East Asia	Park, Haemi; Im, Jungho; Kim, Miae	ARTICLE	620	Improvement of satellite-based estimation of gross primary production through optimization of meteorological parameters and high resolution land cover information at regional scale over East Asia
2019-06	A novel framework of detecting convective initiation combining automated sampling, machine learning, and repeated model tuning from geostationary satellite data	Han, Daehyeon; Lee, Juhyun; Im, Jungho, et al	ARTICLE	766	A novel framework of detecting convective initiation combining automated sampling, machine learning, and repeated model tuning from geostationary satellite data
2019-05	Machine learning approaches for detecting tropical cyclone formation using satellite data	Kim, Minsang; Park, Myung-Sook; Im, Jungho, et al	ARTICLE	679	Machine learning approaches for detecting tropical cyclone formation using satellite data
2019-02	Detection and monitoring of forest fires using Himawari-8 geostationary satellite data in South Korea	Jang, Eunna; Kang, Yoojin; Im, Jungho, et al	ARTICLE	680	Detection and monitoring of forest fires using Himawari-8 geostationary satellite data in South Korea

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