GEO DATA

Volume 7(1); March 2025

Detailed Information for This Issue

Data Articles

Geological Age Data of the Ross Orogeny in the Terra Nova Intrusive Complex, Northern Victoria Land, Antarctica: Sang-Bong Yi, Mi Jung Lee; GEO DATA. 2025;7(1):1-8. Published online February 27, 2025; DOI: https://doi.org/10.22761/GD.2025.0001

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Abstract PDF: This study aims to establish the timing of the formation of the Terra Nova Intrusive Complex (TNIC), located in Terra Nova Bay, northern Victoria Land, Antarctica. The TNIC is paleogeographically situated inland of the Wilson Terrane of northern Victoria Land and was formed during the Paleozoic Ross Orogeny. This study obtained and compiled sensitive high-resolution ion microprobe zircon U-Pb ages for five intrusive bodies in the south-central part of the TNIC. The results clarify the formation age (about 530-470 Ma) of the TNIC and the interrelationships among the various intrusive units, especially those in the south-central region. The Confusion Intrusive Unit was emplaced at 520-515 Ma. The Russell Gabbro intruded into the Confusion Intrusive Unit at 501±3 Ma. Approximately at the same time (about 500 Ma), the Vegetation Intrusive Unit formed, followed by the Abbott Intrusive Unit at about 485 Ma. These findings provide valuable data for interpreting the geological development of the Antarctic Continent and the evolutionary history of the Ross Orogeny.

Characteristics of Plant Communities Distribution in the Estuarine Wetlands along the East and South Coasts of Korea: Yeounsu Chu, Pyoungbeom Kim, Sanghun Lee; GEO DATA. 2025;7(1):9-17. Published online March 24, 2025; DOI: https://doi.org/10.22761/GD.2025.0005

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Abstract PDF: This study was conducted to investigate the distribution patterns and ecological characteristics of plant communities in estuarine wetland in South Korea. A total of 288 estuarine wetlands were surveyed, revealing that vegetated areas accounted for 38.3 km², while non-vegetated areas, predominantly water bodies (133.2 km²), covered 143.8 km². The high proportion of water areas in estuarine wetlands (approximately 70%) contrasts with the 50.8% recorded in inland wetlands, reflecting the challenging conditions for plant establishment due to the continuous mixing of fresh and saline waters. A total of 167 plant communities were identified, with reed (Phragmites australis) communities occupying the largest area (26.0 km²). The analysis of habitat preferences revealed that the majority of the plant communities were categorized as obligate wetland plants (47 species) and facultative wetland plants (12 species), with halophytes playing a significant role in maintaining biodiversity in these ecosystems. Comparative analysis between the East and South coasts showed significant differences in the distribution of wetland and halophytic plant communities, suggesting that the distinct geomorphological and ecological conditions of each region strongly influence plant community structures. These research results will provide a scientific basis for the conservation and management of estuarine wetland ecosystems.

Waterbody Detection and Reservoir Water Level Prediction Using Bayesian Mixture Models with Sentinel-1 GRD Data: DongHyeon Yoon, Ha-Eun Yu, Euiho Hwang, Ki-mook Kang, Gibeom Nam, Jin-Gyeom Kim; GEO DATA. 2025;7(1):18-26. Published online February 5, 2025; DOI: https://doi.org/10.22761/GD.2024.0052

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Abstract PDF: In this study, we used a Bayesian mixture model (BMM) to monitor water surface areas and estimate water levels in Yeongcheon Dam through Sentinel-1 synthetic aperture radar (SAR) imagery. Reservoirs serve vital functions such as flood control, drought mitigation, and ecosystem support, highlighting the importance of precise monitoring of their water surface and level variations, especially in the context of climate change and increased human impact. The BMM method was employed to accurately delineate water boundaries, benefiting from SAR’s capability to capture data regardless of weather conditions. Regression analysis was conducted between the extracted water surface area and observed water levels to create a predictive model, yielding a highly accurate equation with an R² core of 0.981 on the test set. This result indicates a strong correlation between water surface area and water level, affirming the model’s reliability in estimating water levels based solely on surface area data. One of the key findings of this study is that even with a 10 m spatial resolution, reliable water level inferences can be made using water surface area as a proxy. The mean absolute error values obtained validate the model’s capability to monitor water level fluctuations with a satisfactory degree of accuracy. Despite limitations in detecting narrow tributaries or other small-scale features due to SAR resolution, the model performs well overall in monitoring broad water bodies. These findings underscore the potential of Sentinel-1 SAR data for effective reservoir monitoring, especially where real-time water level data may be lacking. For future research, higher-resolution data or complementary algorithms may further enhance detection accuracy for smaller and more complex water features, contributing to more refined water resource management strategies.

KOMPSAT-3/3A Image-text Dataset for Training Large Multimodal Models: Han Oh, Dong-Bin Shin, Dae-Won Chung; GEO DATA. 2025;7(1):27-35. Published online March 19, 2025; DOI: https://doi.org/10.22761/GD.2025.0003

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Abstract PDF: This study aims to improve the accuracy and interpretability of large multimodal models (LMMs) specialized in satellite image analysis by constructing an image-text dataset based on KOMPSAT-3/3A imagery and presenting the results of training using this dataset. Conventional LMMs are primarily trained on general images, limiting their ability to effectively interpret the specific characteristics of satellite imagery, such as spectral bands, spatial resolution, and viewing angles. To address this limitation, we developed an image-text dataset, divided into pretraining and finetuning stages, based on the existing KOMPSAT object detection dataset. The pretraining dataset consists of captions summarizing the overall theme and key information of each image. The fine-tuning dataset integrates metadata -including acquisition time, sensor type, and coordinates- with detailed object detection labels to generate six types of question-answer pairs: detailed descriptions, conversations with varying answer lengths, bounding box identification, multiple choice questions, and complex reasoning. This structured dataset enables the model to learn not only the general context of satellite images but also fine-grained details such as object quantity, location, and geographic attributes. Training with the new KOMPSAT-based dataset significantly improved the model’s accuracy in recognizing regional information and object characteristics in satellite imagery. Finetuned models achieved substantially higher accuracy than previous models, surpassing even the GPT-4o model and demonstrating the effectiveness of a domain-specific dataset. The findings of this study are expected to contribute to various remote sensing applications, including automated satellite image analysis, change detection, and object detection.

GeoAI Dataset for Industrial Park Segmentation from Sentinel-2 Satellite Imagery and GEMS: Sung-Hyun Gong, Hyung-Sup Jung, Geun-han Kim, Geun-Hyouk Han, Il-Hoon Choi, Jin-Sung Hong; GEO DATA. 2025;7(1):36-44. Published online February 13, 2025; DOI: https://doi.org/10.22761/GD.2024.0054

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Abstract PDF: Air pollution in East Asia presents critical environmental and health challenges, particularly in industrial regions affected by domestic and cross-border emissions. This study developed a GEO AI dataset specifically for industrial park segmentation, integrating Sentinel-2 satellite imagery, Geostationary Environment Monitoring Spectrometer (GEMS) geostationary satellite data, and Air Quality Monitoring Network data. Optimized for semantic segmentation tasks with labeled data specifically for industrial park classification, this dataset serves as a foundational asset for the precise identification and spatial tracking of major air pollution sources. We validated the dataset’s applicability using a modified U-Net model, achieving a mean intersection over union of 0.8146 and pixel accuracy of 0.9608, thereby demonstrating its potential as a tool for detecting and monitoring pollutant sources in industrial areas. With future expansion through additional temporal data and diverse pollutant measurements, this dataset is anticipated to support regional air quality monitoring efforts and inform strategies for pollution control across East Asia.

Characteristics of Water Quality and Sediment Distributions on the Northeastern Coast of Jeju Island: Taehee Lee, Hyung Jeek Kim; GEO DATA. 2025;7(1):45-54. Published online March 19, 2025; DOI: https://doi.org/10.22761/GD.2024.0050

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Abstract PDF: Since the 1980s, the number of land-based fish farms on Jeju Island has increased rapidly. With increasing land-based fish farms, a large amount of nutrients from fish farm wastewater is discharged off the coast of Jeju. To understand the characteristics of coastal seawater and the ecological environment on the coast of Jeju, the effect of land-based fish farm effluent on coastal seawater should be evaluated. Temperature, salinity, nutrients, and chlorophyll-a concentration were investigated on the northeastern coast of Jeju during June and July 2023. Nitrate, phosphate, and silicate concentrations in the surface waters were significantly higher in coastal stations than in the outer stations. Unlike the surface waters, nutrient concentrations in the bottom waters are distinctly higher in land-based fish farm effluent stations than in the outer stations. Total organic carbon content in surface sediment was significantly higher in land-based fish farm effluent stations than in the outer stations. This study may provide valuable information for evaluating the impact of land-based fish farm effluent on coastal ecosystems on Jeju Island.

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