GEO DATA

Data Articles

Atmospheric and Ozone Profiling Data Measured with Ozonesonde from 2021 to 2024: Hyungyu Kang, Hyeong-Gyu Kim, Joowan Kim, Taehyoung Lee, Ja-Ho Koo, Sang-Seo Park, Yongjoo Choi, Won-Jin Lee, Sun A Shin, Jinsoo Park; GEO DATA. 2024;6(4):561-577. Published online December 31, 2024; DOI: https://doi.org/10.22761/GD.2024.0041

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Abstract PDF: From 2021 to 2024, balloon-borne in-situ observations were conducted to study the vertical ozone distribution over the Korean Peninsula. The electrochemical concentration cell (ECC) ozonesondes that produce high-resolution ozone profiles from the surface to 30 km based on balloon sounding and KI redox reactions were used, and a total of 123 ozone profiles were produced at Anmyeon, Osan, and Yongin sites. Consecutive daily measurements were made at the Anmyeon site, focusing on ozone transport processes through the Asian summer monsoon. Twice a day (AM, PM) measurements were done at the Yongin site to understand the diurnal cycle of ozone due to photochemical reactions and vertical transport. The ozone data were integrated into netCDF for each measurement year and location, along with the atmospheric profiles of temperature, pressure, humidity, and wind from radiosonde. The ECC ozonesonde provides ozone information with reference-level accuracy, and the data could be further used to improve satellite- and model-based ozone products.

Dataset for Deep Learning-based GEMS Asian Dust Detection: Jin-Woo Yu, Che-Won Park, Won-Jin Lee, Yong-Mi Lee, Yu-Ha Kim, Hyung-Sup Jung; GEO DATA. 2024;6(3):175-185. Published online September 27, 2024; DOI: https://doi.org/10.22761/GD.2023.0049

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In South Korea, Asian dust frequently occurs during the spring, causing various health issues, including respiratory diseases. Consequently, public awareness and concern about air pollutants have increased, leading to demands for improved air quality and accurate forecasting. To meet these demands, the Ministry of Environment has deployed the Geostationary Environment Monitoring Spectrometer (GEMS) on the GK2B satellite to monitor atmospheric pollutants and climate change-inducing substances in real-time. The current GEMS dust product, generated using thresholds of the UV-aerosol index and visible-aerosol index, has shown limitations in accurately detecting suspended particulate matter. This study aims to develop a comprehensive AI dataset for improving GEMS Asian dust detection. Data were collected from January to May 2021, focusing on dates with significant dust events. Label data were meticulously generated through annotations based on outputs from various satellites and groundbased observations. Subsequent data preprocessing and augmentation techniques, including normalization and cut-mix, were applied to enhance the dataset’s robustness and generalizability. To evaluate the dataset, model training was conducted. The results predicted by the model showed improvements over the detection results of existing algorithms. Future datasets will be developed with improved labeling methods and accuracy verification techniques. These dataset improvements are expected to contribute to the development of deep learning models with superior predictive performance compared to current dust detection algorithms.

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GeoAI Dataset for Training a Deep Learning-based GEMS Snow Detection Model
Jin-Woo Yu, Jun-Hyeok Jung, Kyoung-Hee Kang, Yong-Mi Lee, Hyung-Sup Jung
GEO DATA.2024; 6(4): 552. CrossRef

Original Paper

GeoAI Dataset for Industrial Park and Quarry Classification from KOMPSAT-3/3A Optical Satellite Imagery: Che-Won Park, Hyung-Sup Jung, Won-Jin Lee, Kwang-Jae Lee, Kwan-Young Oh, Jae-Young Chang, Moung-jin Lee, Geun-Hyouk Han, Il-Hoon Choi; GEO DATA. 2023;5(4):238-243. Published online December 28, 2023; DOI: https://doi.org/10.22761/GD.2023.0052

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Air pollution is a serious problem in the world, and it is necessary to monitor air pollution emission sources in other neighboring countries to respond to the problem of air pollution spreading across borders. In this study, we utilized domestic and international optical images from KOMPSAT-3/3A satellites to build an AI training dataset for classifying industrial parks and quarries, which are representative sources of air pollution emissions. The data can be used to identify the distribution of air pollution emission sources located at home and abroad along with various state-of-the-art models in the image segmentation field, and is expected to contribute to the preservation of Korea’s air environment as a basis for establishing air-related policies.

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Performance Comparison of Water Body Detection from Sentinel-1 SAR and Sentinel-2 Optical Imagery Using Attention U-Net Model
Il-Hoon Choi, Eu-Ru Lee, Hyung-Sup Jung
Korean Journal of Remote Sensing.2024; 40(5-1): 507. CrossRef

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