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14 "Satellite"
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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
  • 201 View
  • 20 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
  • 203 View
  • 15 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.
Energy Balance Analysis for Water Resources Satellite Operation Orbit Selection
Jingon Bae, Shinhye Moon, Kyungsoo Kim
GEO DATA. 2023;5(3):161-169.   Published online September 27, 2023
DOI: https://doi.org/10.22761/GD.2023.0027
  • 308 View
  • 20 Download
AbstractAbstract PDF
In order to supply enough power for the satellite mission and at the same time to suppress cost increase through over-design, it is necessary to select an appropriate solar array and battery capacity. In the initial stage of satellite design, the required capacity must be analyzed to determine the solar array and battery model, which will be reflected throughout the overall satellite design. This study verifies that the CAS500 satellite platform can provide the power required for the mission in the initial stage of water resources satellite, and furthermore, it found the solar panel and battery capacity required for the water resources satellite. To this end, it was confirmed that the energy balance was satisfied by selecting the worst case one-day mission scenario of the water resources satellite under various conditions.
Improvement of Algal Bloom Identification Using Satellite Images by the Algal Spatial Monitoring and Machine Learning Analysis in a New Dam Reservoir
Hye-Suk Yi, Sunghwa Choi, Dong-Kyun Kim, Hojoon Kim
GEO DATA. 2023;5(3):126-136.   Published online September 25, 2023
DOI: https://doi.org/10.22761/GD.2023.0021
  • 347 View
  • 32 Download
AbstractAbstract PDF
Algal blooms are major issues and an ongoing cause of water quality problems in inland waters globally. In the case of harmful algal blooms, the water temperature rises after nitrogen and phosphorus inflow, which occurs in the summer, is the main cause of the algae bloom. In South Korea, algae monitoring methods have been performed by collecting water in point monitoring stations. Recently, in order to overcome the limitations of these existing monitoring methods, spatial monitoring methods using hyperspectral images and satellite images has been researched. We used satellite images for analysis of the spatial algal variation. The accuracy of algal identification is imperative for effective spatial monitoring of algal blooms in the context of ecological health and assessment. In this study, we generated algal big-data with simultaneously observed chlorophyll-a concentrations based on fluorescence measurement and predicted chlorophyll-a concentrations using 13- band satellite images derived from Sentinel-2. In order to validate the values from the satellite images, we compared them with simultaneously observed chlorophyll-a concentrations based on fluorescence measurement. The goal of this study is to improve the accuracy of predictions induced from satellite images. The analytical techniques were comparatively evaluated. The results showed that Artificial Neural Networks exhibited the best performance among them, improving more than 30% accuracy compared to that of multiple linear regression. Furthermore, the accuracy of identifying algal blooms has been shown to increase at high algal concentrations. In the end, it was successful to create algal bloom maps using a new algorithm to analyze algal bloom management.
Construction of Time-series Displacement Data of Yongdam Dam Based on PSInSAR Analysis of Satellite C-band SAR Images
Taewook Kim, Hyunjin Shin, Jungkyo Jung, Hyangsun Han, Ki-mook Kang, Euiho Hwang
GEO DATA. 2023;5(3):147-154.   Published online September 22, 2023
DOI: https://doi.org/10.22761/GD.2023.0024
  • 412 View
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AbstractAbstract PDF
The increase in water-related disasters due to climate change has a significant impact on the stability of water resource facilities. The displacement of a water resource facility is one of the important indicators to evaluate the stability of the facility. In this study, the time-series displacement of the Yongdam Dam was constructed by applying the persistent scatter interferometric synthetic aperture radar (PSInSAR) technique to the Sentinel-1 C-band SAR images. A sufficient number of persistent scatterers were derived to enable local deformation monitoring of the Yongdam Dam, and the dam showed very small displacement velocity except during the heavy rainfall in August 2020. In the future, C-band SAR imagery from the water resources satellite (Next Generation Medium Satellite 5) is expected to provide accurate displacement data for water resource facilities.
The Introduction of Naju Ground Observation Site Measurement Data and Web Service for Validation of Satellite Value-Added Products
Jong-Sung Ha, Seung-taek Jeong, Hyun-Ok Kim, Sun-Gu Lee, Dae-won Jeong, Jaeil Cho, Seo Ho Shin, Kil-Ja Kim, Dong-Kwan Kim, Jong-Min Yeom
GEO DATA. 2023;5(2):103-109.   Published online June 27, 2023
DOI: https://doi.org/10.22761/GD.2023.0012
  • 407 View
  • 31 Download
AbstractAbstract PDF
The Korea Aerospace Research Institute (KARI) has collaborated with Jeollanamdo Agricultural Research & Extension Services and Chonnam National University to establish a ground observation tower for evaluating the value-added products (such as surface reflectance and Normalized Difference Vegetation Index) and improving algorithms of domestic development satellites (Korea Multi-Purpose Satellite-3, 3A and 7). The ground measurement tower, installed at the Jeollanamdo Naju ground observation site (NGOS), constantly observes surface hyperspectral reflectance and atmospheric information, providing the advantage of real-time algorithm validation improvement when satellite acquires images of the site. The NGOS operates hyperspectral radiometer equipment (6 types), meteorological observation equipment (5 types), sky radiometer (1 type), and eddy flux observation equipment (2 types), along with a web service for display and data processing. The ground observation site equipment that is being installed and operated can be utilized in various fields such as carbon circle, agriculture, environment, atmosphere and climate change, in addition to validation of satellite value-added products. This study aims to introduce KARI NGOS for user data sharing and highlight the characteristics of the measured data.
Articles
GEO-KOMPSAT-2A/2B AMI/GOCI-II/GEMS Data & Products
Sungsik Huh, Kyoung-Wook Jin
GEO DATA. 2022;4(4):39-49.   Published online December 31, 2022
DOI: https://doi.org/10.22761/DJ2022.4.4.005
  • 428 View
  • 25 Download
AbstractAbstract PDF
Two geostationary satellites developed by the Korea Aerospace Research Institute and currently in operation are the GEO-KOMPSAT-2A (GK-2A) and the GEO-KOMPSAT-2B (GK-2B). The main instruments mounted on these satellites are the Advanced Meteorological Imager (AMI), the Geostationary Ocean Color Imager (GOCI-II) and the Geostationary Environment Monitoring Spectrometer (GEMS). This paper briefly introduced the GK-2A and GK-2B programs including measurement principles and elements of the instruments. Moreover, the data formats, operational products, and applications are summarized.
KOMPSAT Optical Image Data Provision and Quality Management
Daesoon Park, Doocheon Seo, Heeseob Kim
GEO DATA. 2022;4(4):28-38.   Published online December 31, 2022
DOI: https://doi.org/10.22761/DJ2022.4.4.004
  • 320 View
  • 22 Download
AbstractAbstract PDF
Korea Aerospace Research Institute (KARI) is conducting continuous quality control to provide reliable optical image products to various users. This paper describes KOrea Multi-Purpose SATellites (KOMPSAT-3 and KOMPSAT-3A) characteristics, operation, and image collection mode in order to enhance satellite image application. Also, image product of the satellites and quality management of the image product are described in this paper. The KOMPSAT-3 launched in 2012 and KOMPSAT-3A launched in 2015 collected many imageries around the world and provide them to users through web. Users can search for images through web catalog and order new imaging task. The KOMPSAT images provided under the KARI control is expected to be great help for earth observation and satellite image application enhancement.
Kompsat-5 Image Data Provision and Quality Management
Dochul Ynag, Horyung Jeong, Doochun Seo
GEO DATA. 2022;4(4):13-19.   Published online December 31, 2022
DOI: https://doi.org/10.22761/DJ2022.4.4.002
  • 464 View
  • 36 Download
AbstractAbstract PDF
The Korea Aerospace Research Institute is conducting continuous quality management to provide reliable Kompsat-5 SAR image products to users. In this paper, the Kompsat-5 satellite operation, data processing, quality management, and data provision were described. The operation and image mode characteristics of the Kompsat-5 satellite from the image point of view were described, and the classification and characteristics of image products provided to users were explained. In addition, image data acquisition, quality index measurement, and its results are described for quality management of SAR images. Finally, it explains how to search for and order Kompsat image product through the ARIRANG system to quickly provide users with image products whose quality has been confirmed through quality management. Kompsat product can be searched and ordered from the ARIRANG Satellite Search and Order System (https://ksatdb.kari.re.kr/arirang/).
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
  • 333 View
  • 20 Download
AbstractAbstract 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.
TanDEM-X-based Ganghwa Tidal Flat High-resolution Topographic Map Construction and Service
Ga Ram Yun, Joo-Hyung Ryu, Kye Lim Kim, Jin Hyung Lee, Seung-Kuk Lee
GEO DATA. 2022;4(1):37-42.   Published online March 31, 2022
DOI: https://doi.org/10.22761/DJ2022.4.1.004
  • 352 View
  • 27 Download
AbstractAbstract PDF
This study extracted the data of Digital Elevation Model (DEM), tidal channel, and tidal channel density, slope based on TanDEM-X satellite of Ganghwa tidal flat. Monitoring and analysis of the decrease in the area of tidal flats in Korea are of great importance, and by judging the efficiency and accuracy in time and space, satellite data were obtained according to the analysis topic of the tidal flats. Since the west coast occupies a large proportion of domestic tidal flats, Ganghwa-do tidal flats were designated as the scope of the study. The produced materials are provided in the form of GEOTIFF(.tif) or Shape(.shp) files. To utilize the tidal flat data constructed in this way, it can be downloaded from the Environmental Big Data website (www.bigdata-environment.kr), an environmental business big data platform.
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
  • 470 View
  • 20 Download
AbstractAbstract 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.
AI Training Dataset for Cloud Detection of KOMPSAT Images
Bo-Ram Kim, Han Oh
GEO DATA. 2020;2(2):56-62.   Published online December 30, 2020
DOI: https://doi.org/10.22761/DJ2020.2.2.008
  • 313 View
  • 18 Download
AbstractAbstract PDF
Clouds that appear inevitably when acquiring optical satellite images hinder the interpretation of surface information, so removing them is a crucial procedure to increase the utilization of satellite images. Currently, for KOMPSAT (Korea Multi-purpose Satellite) images, only the cloud amount by visual measurement is proved for the entire scene and detailed cloud masks are not provided. Since cloud detection is a time-consuming task, we built a cloud dataset for KOMPSAT images so as to develop an algorithm that expedites the task with state-of-the-art artificial intelligent techniques. In the dataset, satellite images were selected from various regions considering that clouds have different characteristics depending on the region, and masks were classified into thin clouds, thick clouds, cloud shadows, and clear sky. The size of dataset is over 4,000 image/mask pairs by an image size of 1000x1000 and one of the largest among publicly available cloud datasets, as of this writing. The dataset is built by a government AI (artificial intelligent) training dataset building program and will be available through the website, aihub.or.kr.
Ground-based data from wheat cropping fields in Australia for development of soil moisture retrieval algorithm using satellite images
SeungJae Lee, SunGu Lee, Dongryeol Ryu
GEO DATA. 2020;2(2):1-4.   Published online December 30, 2020
DOI: https://doi.org/10.22761/DJ2020.2.2.001
  • 388 View
  • 8 Download
AbstractAbstract PDF
Soil moisture is an important data which can be used for crop growth estimation, drought prediction, irrigation, and development of hydrological model. However, it is difficult to obtain soil moisture data from inaccessible area or very large area using only general field campaign. For this reason, many soil moisture retrieval algorithms have been developed based on satellite remote sensing technique. It should be noted that both satellite images and ground-based data for the region of interest are required to effectively develop the soil moisture retrieval algorithm using satellite images. Thus, Korea aerospace research institute, KARI, have collected ground-based data containing soil moisture, soil temperature, and crop height in collaboration with the university of Melbourne from wheat cropping fields in Australia which are suitable for the development of soil moisture retrieval algorithm. The ground-based data was collected from wheat cropping fields containing various types of soils for about 7 months from May 2019 to November 2019.

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