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 Training Deep Learning-Based Optical Satellite Image Matching Model: Jin-Woo Yu, Che-Won Park, Hyung-Sup Jung; GEO DATA. 2023;5(4):244-250. Published online December 28, 2023; DOI: https://doi.org/10.22761/GD.2023.0048

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Satellite imagery is being used to monitor the Earth, as it allows for the continuous provision of multi-temporal observations with consistent quality. To analyze time series remote sensing data with high accuracy, the process of image registration must be conducted beforehand. Image registration techniques are mainly divided into region-based registration and feature-based registration, and both techniques extract the same points based on the similarity of spectral characteristics and object shapes between master and slave images. In addition, recently, deep learning-based siamese neural network and convolutional neural network models have been utilized to match images. This has high performance compared to previous non-deep learning algorithms, but a very large amount of data is required to train a deep learning-based image registration model. In this study, we aim to generate a dataset for training a deep learning-based optical image registration model. To build the data, we acquired Satellite Side-Looking (S2Looking) data, an open dataset, and performed preprocessing and data augmentation on the data to create input data. After that, we added offsets to the X and Y directions between the master and slave images to create label data. The preprocessed input data and labeled data were used to build a dataset suitable for image registration. The data is expected to be useful for training deep learning-based satellite image registration models.

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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

Articles

Mapping of Groundwater Pollution Vulnerability in Entire South Korea Using DRATIC Model: Won-Kyung Baek, Sung-Hwan Park, Jin-Woo Yu, Young-Woong Yoon, Hyung-Sup Jeong; GEO DATA. 2021;3(2):32-38. Published online July 21, 2021; DOI: https://doi.org/10.22761/DJ2021.3.2.005

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Abstract PDF: Groundwater pollution vulnerability was mapped for entire South Korea using groundwater, topography, geology, and soil data. For this, the DRASTIC model developed by the US Environmental Protection Agency was used and the geographic information system (GIS) was used as the basic tool. This groundwater pollution vulnerability map can be usefully used as basic data for groundwater development and conservation management. The constructed data is provided as entire South Korean and regional data, respectively. In addition, in order to expand the accessibility of the data, it is converted and provided in three data formats: ASCII, ArcGIS Grid, and GeoTIFF. All these satellite image analysis data can be downloaded free of charge from the Environment Big Data Platform website (www.bigdata-environment.kr).

Construction of Analyzed Satellite Image and Service: Won-Kyung Baek, Sung-Hwan Park, Jin-Woo Yu, Young-Woong Yoon, Hyung-Sup Jung; GEO DATA. 2020;2(2):45-55. Published online December 30, 2020; DOI: https://doi.org/10.22761/DJ2020.2.2.007

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In this study, 17 types of satellite analysis maps were generated using Landsat-8 and Sentinel-2 satellite images acquired at 2019 and 2020. Totally, 68 of satellite analysis data were produced. The scope of deployment is South Korea as a whole, with a resolution of 30 meters, and the coordinate system is UTM-K coordinates. The established data will be provided in both South Korean and regional data respectively. In addition, it is provided by three data format: ASCII, ArcGIS Grid, and GeoTIFF for enhancing accessibility of the data. All these satellite analysis data can be downloaded free of charge from the Environmental Big Data website (www.bigdata-environment.kr), an environmental business big data platform.

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Expand and Renewal of Analyzed Satellite Image and Service
Young-Woong Yoon, Che-Won Park, Sung-Hyun Gong, Won-Kyung Baek, Hyung-Sup Jung
GEO DATA.2021; 3(4): 32. CrossRef

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