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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, 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
2020-09Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future DirectionsXu, Jin; Quackenbush, Lindi J.; Volk, Timothy A., et alARTICLE36 Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions
2020-07Earth observations and geographic information science for sustainable development goalsIm, JunghoARTICLE72 Earth observations and geographic information science for sustainable development goals
2020-06Object-based landfast sea ice detection over West Antarctica using time series ALOS PALSAR dataKim, Miae; Kim, Hyun-Cheol; Im, Jungho, et alARTICLE96 Object-based landfast sea ice detection over West Antarctica using time series ALOS PALSAR data
2020-05Improvement of spatial interpolation accuracy of daily maximum air temperature in urban areas using a stacking ensemble techniqueCho, Dongjin; Yoo, Cheolhee; Im, Jungho, et alARTICLE71 Improvement of spatial interpolation accuracy of daily maximum air temperature in urban areas using a stacking ensemble technique
2020-05Estimation of All-Weather 1 km MODIS Land Surface Temperature for Humid Summer DaysYoo, Cheolhee; Im, Jungho; Cho, Dongjin, et alARTICLE53 Estimation of All-Weather 1 km MODIS Land Surface Temperature for Humid Summer Days
2020-04Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical modelsPark, Seohui; Lee, Junghee; Im, Jungho, et alARTICLE81 Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models
2020-04Recent ENSO influence on East African drought during rainy seasons through the synergistic use of satellite and reanalysis dataPark, Seonyoung; Kang, Daehyun; Yoo, Cheolhee, et alARTICLE80 Recent ENSO influence on East African drought during rainy seasons through the synergistic use of satellite and reanalysis data
2020-04Comparative Assessment of Various Machine Learning-Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban AreasCho, Dongjin; Yoo, Cheolhee; Im, Jungho, et alARTICLE106 Comparative Assessment of Various Machine Learning-Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas
2020-04Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite ImageryLee, Junghee; Han, Daehyeon; Shin, Minso, et alARTICLE78 Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery
2020-03Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networksKim, Young Jun; Kim, Hyun-Cheol; Han, Daehyeon, et alARTICLE168 Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks
2020-02Estimating ground-level particulate matter concentrations using satellite-based data: a reviewShin, Minso; Kang, Yoojin; Park, Seohui, et alARTICLE125 Estimating ground-level particulate matter concentrations using satellite-based data: a review
2020-01Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite DataLee, Juhyun; Im, Jungho; Cha, Dong-Hyun, et alARTICLE166 Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data
2019-12기상 예보 및 위성 자료를 이용한 우리나라 산불위험지수의 시공간적 고도화강유진; 박수민; 장은나, et alARTICLE170 기상 예보 및 위성 자료를 이용한 우리나라 산불위험지수의 시공간적 고도화
2019-12산불발생위험 추정을 위한 위성기반 가뭄지수 개발박수민; 손보경; 임정호, et alARTICLE118 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 alARTICLE204 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 alARTICLE116 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 alARTICLE177 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 alARTICLE200 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, JunghoARTICLE229 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 alARTICLE254 Zooplankton and micronekton respond to climate fluctuations in the Amundsen Sea polynya, Antarctica

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