사진

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Im, Jungho (임정호)

Department
School of Urban and Environmental Engineering(도시환경공학부)
Research Interests
Remote sensing, Geospatial modeling, Disaster monitoring and management, Climate change
Lab
Intelligent Remote sensing and geospatial Information Science (IRIS) Lab
Website
http://iris.unist.ac.kr/
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Issue DateTitleAuthor(s)TypeViewAltmetrics
2020-01Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite DataLee, Juhyun; Im, Jungho; Cha, Dong-Hyun, et alARTICLE14 Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data
2020Estimating ground-level particulate matter concentrations using satellite-based data: a reviewShin, Minso; Kang, Yoojin; Park, Seohui, et alARTICLE15 Estimating ground-level particulate matter concentrations using satellite-based data: a review
2019-12기상 예보 및 위성 자료를 이용한 우리나라 산불위험지수의 시공간적 고도화강유진; 박수민; 장은나, et alARTICLE38 기상 예보 및 위성 자료를 이용한 우리나라 산불위험지수의 시공간적 고도화
2019-12산불발생위험 추정을 위한 위성기반 가뭄지수 개발박수민; 손보경; 임정호, et alARTICLE35 산불발생위험 추정을 위한 위성기반 가뭄지수 개발
2019-11Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat imagesYoo, Cheolhee; Han, Daehyeon; Im, Jungho, et alARTICLE95 Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images
2019-11Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal VariationLiu, Maolin; Ke, Yinghai; Yin, Qi, et alARTICLE26 Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation
2019-10Delineation of high resolution climate regions over the Korean Peninsula using machine learning approachesPark, Sumin; Park, Haemi; Im, Jungho, et alARTICLE41 Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches
2019-08Retrieval of total precipitable water from Himawari-8 AHI data: A comparison of random forest, extreme gradient boosting, and deep neural networkLee, Yeonjin; Han, Daehyeon; Ahn, Myoung-Hwan, et alARTICLE97 Retrieval of total precipitable water from Himawari-8 AHI data: A comparison of random forest, extreme gradient boosting, and deep neural network
2019-08Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat ImageryLi, Siqi; Quackenbush, Lindi J.; Im, JunghoARTICLE97 Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery
2019-07Zooplankton and micronekton respond to climate fluctuations in the Amundsen Sea polynya, AntarcticaLa, Hyoung Sul; Park, Keyhong; Wahlin, Anna, et alARTICLE131 Zooplankton and micronekton respond to climate fluctuations in the Amundsen Sea polynya, Antarctica
2019-06Improvement of satellite-based estimation of gross primary production through optimization of meteorological parameters and high resolution land cover information at regional scale over East AsiaPark, Haemi; Im, Jungho; Kim, MiaeARTICLE162 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
2019-06A novel framework of detecting convective initiation combining automated sampling, machine learning, and repeated model tuning from geostationary satellite dataHan, Daehyeon; Lee, Juhyun; Im, Jungho, et alARTICLE153 A novel framework of detecting convective initiation combining automated sampling, machine learning, and repeated model tuning from geostationary satellite data
2019-05Machine learning approaches for detecting tropical cyclone formation using satellite dataKim, Minsang; Park, Myung-Sook; Im, Jungho, et alARTICLE195 Machine learning approaches for detecting tropical cyclone formation using satellite data
2019-02Detection and monitoring of forest fires using Himawari-8 geostationary satellite data in South KoreaJang, Eunna; Kang, Yoojin; Im, Jungho, et alARTICLE153 Detection and monitoring of forest fires using Himawari-8 geostationary satellite data in South Korea
2019-01Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South KoreaPark, Seohui; Shin, Minso; Im, Jungho, et alARTICLE141 Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea
2018-12Convolutional Neural Network-Based Land Cover Classification Using 2-D Spectral Reflectance Curve Graphs With Multitemporal Satellite ImageryKim, Miae; Lee, Junghee; Han, Daehyun, et alARTICLE229 Convolutional Neural Network-Based Land Cover Classification Using 2-D Spectral Reflectance Curve Graphs With Multitemporal Satellite Imagery
2018-12기계학습 기반의 IABP 부이 자료와 AMSR2 위성영상을 이용한 여름철 북극 대기 온도 추정한대현; 김영준; 임정호, et alARTICLE261 기계학습 기반의 IABP 부이 자료와 AMSR2 위성영상을 이용한 여름철 북극 대기 온도 추정
2018-11Prediction of drought on pentad scale using remote sensing data and MJO index through random forest over East AsiaPark, Seonyoung; Seo, Eunkyo; Kang, Daehyun, et alARTICLE228 Prediction of drought on pentad scale using remote sensing data and MJO index through random forest over East Asia
2018-08Intercomparison of Downscaling Techniques for Satellite Soil Moisture ProductsKim, Daeun; Moon, Heewon; Kim, Hyunglok, et alARTICLE158 Intercomparison of Downscaling Techniques for Satellite Soil Moisture Products
2018-05Intercomparison of Terrestrial Carbon Fluxes and Carbon Use Efficiency Simulated by CMIP5 Earth System ModelsKim, Dongmin; Lee, Myong-In; Jeong, Su-Jong, et alARTICLE466 Intercomparison of Terrestrial Carbon Fluxes and Carbon Use Efficiency Simulated by CMIP5 Earth System Models

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