GEO DATA

Original Papers

Detailed Bathymetry and Seafloor Backscatter Image Dataset for Monitoring Ecosystem Environment: Southern Ulleungdo: Soon Young Choi, Chang Hwan Kim, Won Hyuck Kim, Chan Hong Park; GEO DATA. 2023;5(4):364-370. Published online December 27, 2023; DOI: https://doi.org/10.22761/GD.2023.0037; Correction in: GEO DATA 2024;6(1):43

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Abstract PDF: We obtained a detailed bathymetry dataset using multi-beam echo sounder around Neunggeol located at Southern part in Ulleungdo. The survey period was from September 1, 2019 and the bathymetry survey was carried out by RV Jangmok No2 of Korea Institute of Ocean Science and Technology. The instrument equipment used for the survey were Kongsbergs’s EM2040 (multi-beam echo sounder). The detailed bathymetry of the study area shows gradually depth change from about 4 to 190 m. The study area has relatively gentle slopes from the coast to 25 m and below about 125 m in depth, but a steep slope from about 25 to 125 m. The rocky bottom zones identified from detailed bathymetry and backscattered seafloor images are distributed in the northeastern sea of the coastal area, the central sea, and around Neunggeol. In particular, Neunggeol and the near large outcropping rock are tumulus-like submarine landforms, forming big vertical walls. The submarine cable buried between Ulleungdo and the Korean mainland appears in the northern seafloor of the survey area. Stopper stones and gravels sediment are distributed around it. The rocky bottom zone has a strong and irregular reflection intensity in the seafloor backscattered image and the sandy sediment zone has a relatively weak reflection intensity and a uniform pattern, so the two zones are well divided. We classified the sediment zones into fine-grained sediments, sandy sediments, and gravel-like sediments by using the different reflection intensities due to differences in sediment particle size in the seafloor image. Detailed bathymetry and seafloor backscattered image dataset around Neunggeol can be used as a basic data for habitat environment mapping.

The Integrated Dataset of Cat (Felis catus) Roadkill by Korea Roadkill Observation System: Sooahn Heo, Il Ryong Kim, Sehee Kim, Hanbi Lee, Euigeun Song; GEO DATA. 2023;5(4):298-303. Published online December 22, 2023; DOI: https://doi.org/10.22761/GD.2023.0042

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Abstract PDF: The incidence of roadkill and habitat fragmentation caused by human development and road networks is on the rise. Cats primarily consume artificial food and engage in hunting near urban areas. Their numbers have an impact on the ecosystem, and cat roadkill incidents are also prevalent. In South Korea, roadkill observations are conducted by the road management agency and the Korea Roadkill Observation System. Over a 3-year period, the number of cat roadkill incidents resulted in 19,973 recorded cases, and the highest rate was in Busan Metropolitan City. Cat roadkill incidents were most prevalent during October and November. Also When analyzed at the local government level, Dangjin-si, Yangpyeong-gun, and Busan Metropolitan City exhibited the highest concentrations. This research offers essential insights for managing the cat population and mitigating cat roadkill occurrences.

Development of Machine Learning Algorithms for Riverside Land Cover Classification Using Synthetic Aperture Radar Satellite Imagery and Terrain Data: Jaese Lee, Dukwon Bae, Young Jun Kim, Jungho Im; GEO DATA. 2023;5(3):119-125. Published online September 25, 2023; DOI: https://doi.org/10.22761/GD.2023.0025

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Abstract PDF: Riverine environments play a crucial role in maintaining the stability of river ecosystems as well as biodiversity. Furthermore, the appropriate management of small rivers has a significant impact not only on stable water supplies but also on water resource management. Wide monitoring of the riverside environment including land covers and their changes is an important issue in water resource management. This study aims to develop a high-resolution (10 m) model for classifying riverside land cover by integrating Sentinel-1 synthetic aperture radar (SAR) data and terrestrial characteristics using machine learning algorithms. We constructed a total of 3,284 landcover reference point datasets near the four major rivers of South Korea with five classes: water, barren, grass, forest, and built-up. The Random Forest and Light Gradient Boosting Machine classification models were developed using eight input variables derived from SAR signal and digital terrain data. The models showed an overall cross-validation accuracy exceeding 80% while maintaining consistent spatial distributions, except for the barren class. The false alarms on barren would be corrected through additional sampling processes and incorporating optical characteristics in further study. The high-resolution riverside land cover maps are expected to contribute to the establishment of a comprehensive management system for water resources such as riverside land cover change detection, river ecosystem monitoring, and flood hazard management. Furthermore, the utilization of the next generation medium satellite 5 (C-band SAR) would improve the performance of riverside land cover classification algorithm in the future.

Establishment of Geographic Information Data of Greenland Glacier Using Fixed-wing Unmanned Aerial Vehicle: Sungjae Lee, Seung Hee Kim, Hyun-Cheol Kim; GEO DATA. 2023;5(1):34-39. Published online March 28, 2023; DOI: https://doi.org/10.22761/GD.2023.0007

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Abstract PDF: In recent decades, the Greenland glacier has experienced significant changes in the environment near the surface due to the increase in surface melting on glacier. In order to quantify these environmental changes, precise spatial information data is necessary. Although digital elevation models using satellite data are widely used to secure data, it is difficult to observe the polar regions by satellite alone due to limitations such as spatial resolution, revisit period, and weather. To overcome these shortcomings, many field geographic surveys using unmanned aerial vehicles are being conducted. In this study, a field survey was conducted on September 14, 2021 to produce high-resolution spatial information in the Russell glacier area located in the Greenland Kangerlussuaq. By matching the acquired aerial image data, orthorectification image with a spatial resolution of about 13 cm/pixel and a digital surface model are produced. This data is expected to be utilized as basic spatial data for Russell glacier runoff and topographical changes, and it is expected to be used as data that can grasp changes in time and spatial through continuous data accumulation.

Articles

KOMPSAT-5 GNSS Radio Occultation Data Operations: Okchul Jung, Jaedong Seong, Myeongshin Lee, Daewon Chung; GEO DATA. 2022;4(3):1-7. Published online September 30, 2022; DOI: https://doi.org/10.22761/DJ2022.4.3.001

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Abstract PDF: The Korea Aerospace Research Institute launched KOMPSAT-5 on August 22, 2013, and has been operating for 10 years. KOMPSAT-5 has SAR (Synthetic Aperture Radar) for earth observation missions, and collects data necessary for earth atmosphere analysis through GNSS RO (Radio Occultation) receivers. RO data can be used for numerical weather forecast model based on temperature, pressure, and humidity by calculating the vertical distribution of atmospheric information. As a part of the Korea-US science and technology cooperation, KARI has been providing RO data of KOMPSAT-5 to the United States NOAA (National Oceanic and Atmospheric Administration) in near-real time since 2018. To this end, KARI receives telemetry data from the satellite about 12 times a day using 3 ground stations from Daejeon, Alaska in the U.S., and Sodankyla in Finland. The pre-processed data is being provided to both the UCAR (University Corporation for Atmospheric Research) in the U.S. and the KASI (Korea Astronomy and Space Science Institute). In this paper, radio occlusion data of KOMPSAT-5 is introduced, and system configuration, operation concepts for providing near-real time data and its application are also presented.

Construction of a Training Dataset for Vessel Distribution Prediction: The Northern Seas of Jeju Island: Yonggil Park, Taehoon Kim, Hyeon-Gyeong Han, Cholyoung Lee; GEO DATA. 2022;4(2):37-46. Published online June 30, 2022; DOI: https://doi.org/10.22761/DJ2022.4.2.004

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Abstract PDF: Recently, interest in maritime accidents and safety-related research, such as preventing collisions between marine vessels, detecting illegal vessels, and predicting vessel routes, is increasing. Vessel location data-based vessel distribution map can support decision-making for maritime safety management, and if the vessel distribution can be predicted, it is possible to take a preemptive response for maritime security such as fishing safety management and illegal fishing prevention. In this study, a training dataset for vessel distribution prediction was constructed by collecting V-Pass data, weather warnings, and marine environment data. The result of resampling of reporting interval of vessel location data was mapped to grid data to evaluate the vessel density, and a total of 1,314,000 of training data were constructed for the study area. In the future, research to evaluate the accuracy by performing vessel distribution prediction modeling should be conducted.

Development of the Method for Authentication of the Genus Anguilla Sold on the Korean Fish Market and Current Status of Distribution: Yo-Soon Jang; GEO DATA. 2022;4(1):16-26. Published online March 31, 2022; DOI: https://doi.org/10.22761/DJ2022.4.1.002

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Abstract PDF: This study was carried out to identify the mislabeling of Japanese eel (Anguilla japonica) sold on the fish markets in Korea and to develop a method for determining the authenticity of fresh and trimmed eels. Between January and December 2018, 31 test samples were collected from restaurants and fish markets in Seoul, South Korea, and the collected samples were analyzed. The results showed that over two-thirds of the samples tested were mislabeled. Molecular identification of 31 test samples revealed that 10 Anguilla japonica, 9 Anguilla Anguilla, 2 Anguilla rostrate, 1 Anguilla marmorata, 7 Ophichthus remiger, 1 Brachysomophis crocodilinus, and 1 Conger myriaster. We have developed the NdeⅠPCR-RFLP assay for determining the authentication of fresh and trimmed eels sold on the fish markets in Korea, and this assay enables rapidly and accurately identify the genus Anguilla.

Radiometric Distortion Corrected Radar Backscattering Coefficient Data over Ilam, Iran using Kompsat-5 SAR Image: Dochul Yang; GEO DATA. 2021;3(4):28-31. Published online December 31, 2021; DOI: https://doi.org/10.22761/DJ2021.3.4.004

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Abstract PDF: Flattening gamma naught was calculated using Korea Multipurpose Satellite 5 (KOMPSAT-5, K5) by correcting the radiometric distortion caused by geometric distortion over Ilam, Iran. The flattening gamma naught is not only the SAR core observation of Analysis Ready Data (ARD), which is utilized for artificial intelligence and big data, but also the basis for all fields of application that use the SAR brightness by providing the backscattering values only from surface characteristics. The flattening gamma naught data is provided with the same resolution as that of the K5 SAR image, so the data over the Ilam, Iran have the spatial resolution of the K5 Wide Swath mode of 20 m. Shuttle Radar Topography Mission (SRTM) DEM with a resolution of 30 m was oversampled to generate the flattening gamma naught, and shadow areas where flattening gamma naught generation was not possible were identified using GIM layer information provided with the K5 image. In order to determine the reliability of the calculated flattening gamma naught, histogram analysis and tendency according to the incident angle were investigated, and the performance was verified by comparing it with other backscattering coefficients. Details of the algorithm and procedure are presented in previous studies and reference papers.

Opinion

Quality Control and Verification of Artificial Intelligence Data: Myeung Un Kim, Hyojung Ahn; GEO DATA. 2021;3(3):35-40. Published online September 30, 2021; DOI: https://doi.org/10.22761/DJ2021.3.3.004

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1 Citations

Abstract PDF

High-quality artificial intelligence (AI) data provides accurate information for developing AI models. These results in increasing the efficiency of the model. On the other hand, if low-quality data is used, it may adversely affect the development of AI models. To improve the quality of our research, we need to increase the quality of AI data. This is possible through systematic quality control and verification of the data. Currently, there are various guidelines such as the data quality act of the US, the ISO 8000 series of the International Organization for Standardization, and the Big Data quality verification standard of the United Nations, as well as Korea's database quality certification. In this study, the current status of data quality management is identified and its implications are considered.

Citations

Citations to this article as recorded by

Synthetic data generation with hybrid quantum-classical models for the financial sector
Otto M. Pires, Mauro Q. Nooblath, Yan Alef C. Silva, Maria Heloísa F. da Silva, Lucas Q. Galvão, Anton S. Albino
The European Physical Journal B.2024;[Epub] CrossRef

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