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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
  • 249 View
  • 20 Download
AbstractAbstract PDF
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.
Original Papers
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
  • 998 View
  • 68 Download
AbstractAbstract 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.
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
  • 927 View
  • 54 Download
AbstractAbstract PDF
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.
Article
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):32-48.   Published online December 31, 2021
DOI: https://doi.org/10.22761/DJ2021.3.4.005
  • 1,030 View
  • 27 Download
AbstractAbstract PDF
In this study, additional satellite analysis data in 2019, 2020, and 2021 were generated using Landsat-8 and Sentinel-2 satellite images. We additionally employed 19 types of satellite analysis methods, and generated totally 57 cases of satellite analysis data for three years. In addition, 34 types of satellite analysis data were updated using 2021 satellite data. In conclusion, a total of 91 cases of satellite analysis data were generated. The coverage of the study is the entire South Korea. The spatial resolution and the coordinate system were 30 m and UTM-K (EPSG: 5179) respectively. The products are provided as the entire South Korean and regional data, respectively. In addition, it is provided in three data types: ASCII, ArcGIS Grid, and GeoTIFF as same as last distribution. All satellite image 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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