GEO DATA

Data Article

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

Article

GEO-KOMPSAT-2A/2B AMI/GOCI-II/GEMS Data & Products: Sungsik Huh, Kyoung-Wook Jin; GEO DATA. 2022;4(4):39-49. Published online December 31, 2022; DOI: https://doi.org/10.22761/DJ2022.4.4.005

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Abstract PDF

Two geostationary satellites developed by the Korea Aerospace Research Institute and currently in operation are the GEO-KOMPSAT-2A (GK-2A) and the GEO-KOMPSAT-2B (GK-2B). The main instruments mounted on these satellites are the Advanced Meteorological Imager (AMI), the Geostationary Ocean Color Imager (GOCI-II) and the Geostationary Environment Monitoring Spectrometer (GEMS). This paper briefly introduced the GK-2A and GK-2B programs including measurement principles and elements of the instruments. Moreover, the data formats, operational products, and applications are summarized.

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Hyperspectral Image-Based Identification of Maritime Objects Using Convolutional Neural Networks and Classifier Models
Dongmin Seo, Daekyeom Lee, Sekil Park, Sangwoo Oh
Journal of Marine Science and Engineering.2024; 13(1): 6. CrossRef

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