H
Hyun-Woo Jo
Researcher at Korea University
Publications - 11
Citations - 254
Hyun-Woo Jo is an academic researcher from Korea University. The author has contributed to research in topics: Computer science & Environmental science. The author has an hindex of 3, co-authored 3 publications receiving 148 citations.
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Journal ArticleDOI
International benchmarking of terrestrial laser scanning approaches for forest inventories
Xinlian Liang,Juha Hyyppä,Harri Kaartinen,Harri Kaartinen,Matti Lehtomäki,Jiri Pyörälä,Jiri Pyörälä,Norbert Pfeifer,Markus Holopainen,Markus Holopainen,Gábor Brolly,Pirotti Francesco,Jan Hackenberg,Jan Hackenberg,Huabing Huang,Hyun-Woo Jo,Masato Katoh,Luxia Liu,Martin Mokroš,Jules Morel,Kenneth Olofsson,Jose Poveda-Lopez,Jan Trochta,Di Wang,Jinhu Wang,Zhouxi Xi,Bisheng Yang,Guang Zheng,Ville Kankare,Ville Kankare,Ville Luoma,Ville Luoma,Xiaowei Yu,Liang Chen,Mikko Vastaranta,Mikko Vastaranta,Mikko Vastaranta,Ninni Saarinen,Ninni Saarinen,Yunsheng Wang +39 more
TL;DR: The main objectives of this benchmarking study are to evaluate the potential of applying TLS in characterizing forests, to clarify the strengths and the weaknesses of TLS as a measure of forest digitization, and to reveal the capability of recent algorithms for tree-attribute extraction.
Journal ArticleDOI
Deep Learning Applications on Multitemporal SAR (Sentinel-1) Image Classification Using Confined Labeled Data: The Case of Detecting Rice Paddy in South Korea
Hyun-Woo Jo,Sujong Lee,Eunbeen Park,Chul Hee Lim,Cholho Song,Halim Lee,Young Jin Ko,Sungeun Cha,Hoonjoo Yoon,Woo-Kyun Lee +9 more
TL;DR: Three kinds of deep learning applications—data augmentation, semisupervised classification, and domain-adapted architecture—were tested in an effort to overcome the limitation of insufficient labeled data and indicated that all combinations of the applications can contribute to increase classification performance, even though the uncertainty involved in imitating or utilizing unlabeled data remains.
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Phenological Classification Using Deep Learning and the Sentinel-2 Satellite to Identify Priority Afforestation Sites in North Korea
TL;DR: The area of deforested land in North Korea is determined through the vegetation phenological classification using high-resolution satellite imagery and deep learning algorithms and it will be possible to contribute to carbon neutrality and greenhouse gas reduction on the Korean Peninsula level through optimal afforestation by using these outcomes.
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Assessment of COVID-19 Impacts on Air Quality in Ulaanbaatar, Mongolia, Based on Terrestrial and Sentinel-5P TROPOMI Data
TL;DR: In this paper , the impact of three sequential strict-lockdowns of COVID-19 measures on the air pollutants including NO2, SO2, PM10, and PM2.5 in Ulaanbaatar, Mongolia during November 2020-February 2021 based on air quality network and satellite data was revealed.
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How to manage land use conflict between ecosystem and sustainable energy for low carbon transition?: Net present value analysis for ecosystem service and energy supply
TL;DR: In this article , a net present value (NPV) analysis for solving land use conflict by comparing monetary value according to different land use cases was conducted, and two land use scenarios were investigated: 1) land cover maintained (forest or agricultural land) and 2) land use change for solar energy generation.