GEO DATA

DATA ARTICLE

GeoAI Dataset for Industrial Park Segmentation from Sentinel-2 Satellite Imagery and GEMS: Sung-Hyun Gong, Hyung-Sup Jung, Geun-han Kim, Geun-Hyouk Han, Il-Hoon Choi, Jin-Sung Hong; Received November 20, 2024 Accepted January 7, 2025 Published online February 13, 2025; DOI: https://doi.org/10.22761/GD.2024.0054 [Epub ahead of print]

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Abstract PDF: Air pollution in East Asia presents critical environmental and health challenges, particularly in industrial regions affected by domestic and cross-border emissions. This study developed a GEO AI dataset specifically for industrial park segmentation, integrating Sentinel-2 satellite imagery, Geostationary Environment Monitoring Spectrometer (GEMS) geostationary satellite data, and Air Quality Monitoring Network data. Optimized for semantic segmentation tasks with labeled data specifically for industrial park classification, this dataset serves as a foundational asset for the precise identification and spatial tracking of major air pollution sources. We validated the dataset’s applicability using a modified U-Net model, achieving a mean intersection over union of 0.8146 and pixel accuracy of 0.9608, thereby demonstrating its potential as a tool for detecting and monitoring pollutant sources in industrial areas. With future expansion through additional temporal data and diverse pollutant measurements, this dataset is anticipated to support regional air quality monitoring efforts and inform strategies for pollution control across East Asia.

Data Article

GeoAI Dataset for Urbanized Area Segmentation from Landsat 8/9 Satellite Imagery and GEMS: Sung-Hyun Gong, Hyung-Sup Jung, Geun-Han Kim, Geun-Hyouk Han, Il-Hoon Choi, Jin-Sung Hong; GEO DATA. 2024;6(4):478-486. Published online December 31, 2024; DOI: https://doi.org/10.22761/GD.2024.0053

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Abstract PDF: In South Korea, air pollution has emerged as a pressing social issue, necessitating data-driven approaches to monitor sources of air pollutants. This study constructed a GEO AI dataset for detecting air pollution sources in urbanized areas, utilizing Landsat 8/9 satellite imagery, Geostationary Environment Monitoring Spectrometer geostationary satellite data, and air quality monitoring network data. The dataset is optimized for semantic segmentation tasks, including labeled data for urban area segmentation, and is designed to enable precise detection of pollution sources within urban regions by integrating satellite imagery and air quality information. Using this dataset, we applied a modified U-Net model to classify pollutant sources in urbanized areas, achieving high performance with an mIoU of 0.8592 and pixel accuracy of 0.9433. These results demonstrate the effectiveness of the GEO AI dataset as a tool for identifying and managing major pollution sources, providing foundational data for air quality monitoring and policy development across South Korea and East Asia. With further integration of additional air pollution data, this dataset is expected to contribute to long-term air quality management and the mitigation of health impacts associated with pollution.

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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Abstract PDF

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.

Citations

Citations to this article as recorded by

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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