GEO DATA

Data Articles

Classification of Subdivision Land Use and Land Cover Using Deep Learning Models: Bongseok Jeong, Sunmin Lee, Moung-jin Lee; GEO DATA. 2024;6(4):535-551. Published online December 31, 2024; DOI: https://doi.org/10.22761/GD.2024.0059

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Abstract PDF: Land cover provides crucial information related to biological geography, ecological climatology, and human activities. In the past, land cover mapping was performed based on visual interpretation, but it had limitations in terms of time and cost. Recently, it has become possible to create land cover maps with higher temporal resolution over wider areas using artificial intelligence-based models. The accuracy and reliability of AI model-based land cover maps increase with the amount of training data, but it is difficult to acquire large amounts of data due to the time required for label data annotation. In South Korea, the Environmental Geographic Information Service provides self-learning data consisting of aerial orthoimages and subdivision land cover classification level label data, making it possible to collect high-quality data. Therefore, this study examined the feasibility of self-learning data by building and evaluating a U-Net-based land cover classification model for waterfront areas using self-learning data. The trained model showed relatively low performance with an F-1 score of 0.61 for training data and 0.31 for test data. The model’s low performance is thought to be due to insufficient training caused by the large number of classification categories (34) and data imbalance between categories. Although the model performance using self-learning data was low, it is believed that model performance can be improved by grouping classification categories according to research purposes or resolving data imbalance through data augmentation techniques. Therefore, self-learning data is expected to be utilized in various studies using land cover.

Distribution of Legally Protected Mammals in South Korea Based on Post-environmental Impact Assessment over 5 Years: 2017-2021: Sunmin Lee, Jinhee Lee, Moung-Jin Lee, Jeong-Cheol Kim; GEO DATA. 2024;6(4):529-534. Published online December 31, 2024; DOI: https://doi.org/10.22761/GD.2024.0045

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Abstract PDF: Biodiversity is essential for ecosystem stability and human survival, playing a critical role in ecosystem services, resource cycling, and climate regulation. However, recent challenges such as habitat degradation and fragmentation due to development or climate change have placed biodiversity under threat. Thus, many countries are actively working to preserve and restore biodiversity through efforts to protect endangered species. In line with this international movement, South Korea has been collecting baseline data on wildlife habitat distribution through the National Natural Environment Survey. However, the extended survey cycle and regional timing differences pose limitations on regularly updating endangered wildlife habitat status. Therefore, this study aimed to utilize postenvironmental impact assessment data within the environmental impact assessment system, which monitors various environmental factors including the natural and living environments. Habitat data for legally protected mammals across South Korea was collect on three species: the yellow-throated marten (Martes flavigula), the leopard cat (Prionailurus bengalensis), and the Eurasian otter (Lutra lutra). These species play an essential role in maintaining ecosystem health and biodiversity, making habitat conservation crucial. This study is expected to provide foundational data to support systematic management and reliable conservation policies for legally protected mammals in South Korea.

Original Papers

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

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

Detection of Floating Debris in the Lake Using Statistical Properties of Synthetic Aperture Radar Pulses: Donghyeon Yoon, Ha-eun Yu, Moung-Jin Lee; GEO DATA. 2023;5(3):185-194. Published online September 27, 2023; DOI: https://doi.org/10.22761/GD.2023.0032

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Abstract PDF: This study developed the European Space Agency (ESA) Setinel-1 Ground Range Detected (GRD) time series analysis model for monitoring floating debris in lake areas through Google Earth Engine Application Programming Interface. The study aims to monitor floating debris caused by heavy rainfall efficiently. Regarding water resources and water quality management, floating debris from multipurpose dams requires continuous monitoring from the initial generation stage. In the study, a Synthetic Aperture Radar (SAR) time series analysis model that is easy to identify water bodies was developed due to low accessibility in large areas. Although SAR satellite images could be used to observe inland water environments, debris detection on water surface surfaces has yet to be studied. For the first time, this study detected floating debris patches in a wide range of lakes from GRD imagery acquired by ESA’s Sentinel-1 satellite. It demonstrated the potential to distinguish them from naturally occurring materials such as invasive floating plants. In this study, the case of Daecheong Dam, in which predicted floating debris was detected after heavy rain using Sentinel-1 GRD data, is presented. It could quickly detect various floating debris flowing into dams used as a source of drinking water and serve as a reference for establishing a collection plan.

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