Hyung-Sup Jung

Professor

Seoul National University

South Korea

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Hyung-Sup Jung is a Professor at the University of Seoul, South Korea. His research focuses on GeoAI, deep learning applications in environmental monitoring, and image segmentation techniques, as evidenced by his recent publications on urbanized area segmentation and PM2.5 vulnerability analysis. He has made significant contributions to the field through the development of datasets and models for satellite and drone imagery analysis.

Articles (27)

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Collaborators (1)

Won-Kyung Baek

Korea Institute of Ocean Science and Technology

SOUTH KOREA

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Hyung-Sup Jung

Research Interests

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About

Articles (27)

Erratum to: GeoAI Dataset for Industrial Park Segmentation from Sentinel-2 Satellite Imagery and GEMS

Erratum to: GeoAI Dataset for Urbanized Area Segmentation from Landsat 8/9 Satellite Imagery and GEMS

PM2.5 Vulnerability Areas Analysis Using Deep Neural Network: Application to Seoul City

Efficient Vectorization Method for Design Drawings Using UAV Images and Deep Learning Segmentation

Improving Inland Wetland Classification Performance of Drone Imagery-Based TransUNet Model Using Multi-Class Data Balancing Technique

Semantic Segmentation of Urbanized Areas Using Multi-Encoder U-Net Based on Multi-Modal Data

GeoAI Dataset for Industrial Park Segmentation from Sentinel-2 Satellite Imagery and GEMS

Chimney detection and size estimation from high-resolution optical satellite imagery using deep learning models

The Cheonji Lake GeoAI Dataset based in Optical Satellite Imagery: Landsat-5/-7/-8 and Sentinel-2

A Robust Bad-Pixel Radiance Reconstruction for the Geostationary Environment Monitoring Spectrometer (GEMS) – Influences on Aerosol Retrieval

Land Cover Classification From RGB and NIR Satellite Images Using Modified U-Net Model

Deep Learning Training Data for Phase Unwrapping of SAR Interferograms

GeoAI Dataset for Training a Deep Learning-based GEMS Snow Detection Model

Advanced Sensor Technologies in Geospatial Sciences and Engineering 2020

Special Issue on Selected Papers from “International Symposium on Remote Sensing 2021”

Forest Vertical Structure Mapping Using Multi-Seasonal UAV Images and Lidar Data via Modified U-Net Approaches

Mapping Tree Species Using CNN from Bi-Seasonal High-Resolution Drone Optic and LiDAR Data

GeoAI Dataset for Industrial Park and Quarry Classification from KOMPSAT-3/3A Optical Satellite Imagery

GeoAI Dataset for Rural Hazardous Facilities Segmentation from KOMPSAT Ortho Mosaic Imagery

GeoAI Dataset for Training Deep Learning-Based Optical Satellite Image Matching Model

The Cheonji Lake GeoAI Dataset Based in Synthetic Aperture Radar Images: TerraSAR-X, Sentinel-1 and ALOS PALSAR-2

Forest Vertical Structure Mapping Using Two-Seasonal Optic Images and LiDAR DSM Acquired from UAV Platform through Random Forest, XGBoost, and Support Vector Machine Approaches

Improved Calibration of Wind Estimates from Advanced Scatterometer MetOp-B in Korean Seas Using Deep Neural Network

Mapping Forest Vertical Structure in Sogwang-ri Forest from Full-Waveform Lidar Point Clouds Using Deep Neural Network

Performance Comparison of Oil Spill and Ship Classification from X-Band Dual- and Single-Polarized SAR Image Using Support Vector Machine, Random Forest, and Deep Neural Network

A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network

Remote Sensing and Geoscience Information Systems Applied to Groundwater Research

Collaborators (1)

Won-Kyung Baek

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