GEO DATA

Data Articles

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):552-560. Published online December 31, 2024; DOI: https://doi.org/10.22761/GD.2024.0060

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Abstract PDF: The Geostationary Environment Monitoring Spectrometer (GEMS) observes air quality across East Asia from an altitude of approximately 36,000 km, analyzing the spatiotemporal distribution of atmospheric pollutants that spread beyond localized regions. GEMS currently provides 21 core air quality-related products, most of which are derived from Level 1C data, which has undergone geometric and radiometric correction. For enhanced accuracy in air quality analysis, precise surface reflectance estimation is essential. However, high-reflectance elements, such as snow, interfere with the accurate estimation of radiance values, necessitating precise detection of such areas. Despite this, GEMS relies solely on the ultraviolet and partial visible bands, lacking the infrared bands crucial for snow detection, and it has no proprietary snow detection algorithm, instead utilizing near-real-time ice and snow extent data from the U.S. National Snow and Ice Data Center. Recently, deep learning techniques have shown potential in image processing, outperforming traditional algorithms, which could address these limitations. However, there is currently no deep learning training dataset available for snow detection specifically for GEMS. To address this issue, this study developed a GeoAI dataset for training a deep learning-based snow detection model for GEMS. In this research, we constructed input data using GEMS Level 1C data and generated label data based on GEMS, Advanced Meteorological Imager, and MODIS snow cover data. The snow detection dataset developed in this study is expected to address the snow detection limitations of GEMS, providing foundational data to enhance the reliability of future geostationary satellite-based air quality research.

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 Rural Hazardous Facilities Segmentation from KOMPSAT Ortho Mosaic Imagery: Sung-Hyun Gong, Hyung-Sup Jung, Moung-Jin Lee, Kwang-Jae Lee, Kwan-Young Oh, Jae-Young Chang; GEO DATA. 2023;5(4):231-237. Published online December 28, 2023; DOI: https://doi.org/10.22761/GD.2023.0054

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In South Korea, rural areas have been recognized for their potential as sustainable spaces for the future, but they are currently facing major problems. Unplanned construction of facilities such as factories, livestock facilities, and solar panels near residential areas is destroying the rural environment and deteriorating the quality of life of residents. Detection and monitoring of rural facilities are necessary to prevent disorderly development in rural areas and to manage rural space in a planned manner. In this study, satellite imagery data was utilized to obtain information on rural areas, which is useful for observing large areas and monitoring time series changes compared to field surveys. In this study, KOMPSAT ortho-mosaic optical imagery from 2019 and 2020 were utilized to construct AI training datasets for rural hazardous facilities segmentation for Seosan, Anseong, Naju, and Geochang areas. The dataset can be used in image segmentation models to classify rural facilities and can be used to monitor potentially hazardous facilities in rural areas. It is expected to contribute to solving rural problems by serving as the basis for rural planning.

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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

Article

AI Dataset for Road Detection using KOMPSAT Images: Hoonhee Lee, Han Oh; GEO DATA. 2022;4(1):43-48. Published online March 31, 2022; DOI: https://doi.org/10.22761/DJ2022.4.1.005

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Abstract PDF: Information on shape and type of road present in an optical image of satellite is useful for digital mapping and monitoring of road changes. Processing and structuring optical image data collected from payloads mounted on KOMPSAT 3 and 3A can accelerate the development of road detection algorithms and the extraction of road information using them. In particular, if it is built with a learning dataset for AI (Artificial Intelligence) prepared to apply deep learning technology, the latest artificial intelligence technology in the field of computer science can be spun off to the field of satellite image-based road detection to attempt a wide range of analysis. Korea Aerospace Research Institute constructed an image dataset for AI learning using satellite optical images with Korean companies, and this paper explains the type and size of datasets along with examples of the use of the dataset. The established data can be used through the website, aihub.or.kr.

Opinion

Quality Control and Verification of Artificial Intelligence Data: Myeung Un Kim, Hyojung Ahn; GEO DATA. 2021;3(3):35-40. Published online September 30, 2021; DOI: https://doi.org/10.22761/DJ2021.3.3.004

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High-quality artificial intelligence (AI) data provides accurate information for developing AI models. These results in increasing the efficiency of the model. On the other hand, if low-quality data is used, it may adversely affect the development of AI models. To improve the quality of our research, we need to increase the quality of AI data. This is possible through systematic quality control and verification of the data. Currently, there are various guidelines such as the data quality act of the US, the ISO 8000 series of the International Organization for Standardization, and the Big Data quality verification standard of the United Nations, as well as Korea's database quality certification. In this study, the current status of data quality management is identified and its implications are considered.

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Synthetic data generation with hybrid quantum-classical models for the financial sector
Otto M. Pires, Mauro Q. Nooblath, Yan Alef C. Silva, Maria Heloísa F. da Silva, Lucas Q. Galvão, Anton S. Albino
The European Physical Journal B.2024;[Epub] CrossRef

Article

Dataset for Water Body Detection Using Satellite SAR Images: SeungJae Lee, Han Oh; GEO DATA. 2021;3(2):12-19. Published online July 21, 2021; DOI: https://doi.org/10.22761/DJ2021.3.2.002

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Abstract PDF: Satellite synthetic aperture radar (SAR) generates valid image information in all-weather. Thus, it can be effectively used for near real-time monitoring and damage analysis of flood areas which always involve overcast skies. Water body detection (WBD) using SAR images can be implemented by various techniques which discriminate electromagnetic characteristics between water and non-water areas. Especially, semantic segmentation exploiting artificial intelligence techniques can be used to develop a high-performance WBD model. To this end, Korea Aerospace Research Institute has built an WBD dataset using KOMPSAT-5 images. The dataset is currently available through the website, aihub.or.kr.

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