사진

  • ResearcherID
  • ORCiD
  • Scopus
  • Google Citations

Im, Jungho (임정호)

Department
School of Urban and Environmental Engineering(도시환경공학부)
Lab
This table browses all dspace content
Issue DateTitleAuthor(s)TypeViewAltmetrics
2020-06Object-based landfast sea ice detection over West Antarctica using time series ALOS PALSAR dataKim, Miae; Kim, Hyun-Cheol; Im, Jungho, et alARTICLE38 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 alARTICLE9 Improvement of spatial interpolation accuracy of daily maximum air temperature in urban areas using a stacking ensemble technique
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 alARTICLE30 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 alARTICLE27 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 alARTICLE35 Comparative Assessment of Various Machine Learning-Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas
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 alARTICLE70 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 alARTICLE79 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 alARTICLE98 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 alARTICLE101 기상 예보 및 위성 자료를 이용한 우리나라 산불위험지수의 시공간적 고도화
2019-12산불발생위험 추정을 위한 위성기반 가뭄지수 개발박수민; 손보경; 임정호, et alARTICLE87 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 alARTICLE153 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 alARTICLE70 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 alARTICLE99 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 alARTICLE150 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, JunghoARTICLE169 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 alARTICLE192 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, MiaeARTICLE237 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 alARTICLE220 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 alARTICLE272 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 alARTICLE220 Detection and monitoring of forest fires using Himawari-8 geostationary satellite data in South Korea

MENU