GEO DATA

Data Articles

Construction of AI-based Dataset for Rock Type Classification and Joint Detection Using Bedrock Drill Core Images: Sooyeon Han; GEO DATA. 2025;7(3):196-206. Published online September 29, 2025; DOI: https://doi.org/10.22761/GD.2025.0028

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This study aims to address frequent ground subsidence issues in urban areas by constructing an artificial intelligence (AI)-based dataset for rock type classification and joint detection using bedrock drill core images. In Korea, current ground assessments heavily rely on subjective and qualitative interpretations, lacking accuracy and objectivity. To overcome this limitation, high-resolution images of bedrock drill core samples were collected and meticulously annotated, resulting in a comprehensive dataset comprising 661,425 images. The dataset includes 550,080 images for rock type classification and 111,345 images for joint detection, with labeling data provided in JSON format. The rock type classification dataset is categorized into igneous, metamorphic, and sedimentary rocks, while the joint detection dataset employs a polygon segmentation method. The efficacy of the dataset was validated by applying ResNet152 V2 for rock classification and Deeplab V3+ for joint detection models, achieving a Top-1 Accuracy of 90.25% and an intersection over union (IoU) of 84.04%, respectively. The constructed dataset is publicly available via AI-Hub and is expected to significantly contribute to the development of quantitative and rapid bedrock evaluation technologies in research and industry.

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GeoBot: Construction of a Structured Korean Question-Answer Dataset for Conversational Artificial Intelligence Services on the Geo Big Data Open Platform
Sooyeon Han, Hankyung Bae, JongGyu Han
GEO DATA.2026; 8(1): 19. CrossRef

Unmanned Aerial Vehicle Photogrammetry Based Dataset of Halophyte Distribution in Jujin Estuary: Donguk Lee, Yeongjae Jang, Joo-Hyung Ryu, Hyeong-Tae Jou, Keunyong Kim; GEO DATA. 2024;6(4):505-511. Published online December 4, 2024; DOI: https://doi.org/10.22761/GD.2024.0012

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The importance of blue carbon is significant in terms of climate change mitigation and marine ecosystem conservation, and halophyte acts as a crucial reservoir for this blue carbon. Accordingly, this study utilized unmanned aerial vehicle (UAV) optical sensors to create a distribution map of vegetation in the natural salt marsh of the Jujin estuary. The optical images captured from a UAV at an altitude of 50 m provide ultra-high-resolution optical information with a ground sampling distance of 0.6 cm. Based on these images, a U-Net model was trained to classify Phragmites communis and Suaeda maritima, generating a classification map of the mixed habitats of salt marsh plants. The areas of Phragmites communis and Suaeda maritima in the Jujin- Cheon region were found to be 6,653.23 m² and 1,409.08 m², respectively. The classification results were validated using field control point data, confirming an approximate classification accuracy of 92%.

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High-Resolution Mapping of Thermal Effluents in Inland Streams and Coastal Seas Using UAV-Based Thermal Infrared Imagery
Sunyang Baek, Junhyeok Jung, Hyung-Sup Jung
Remote Sensing.2026; 18(8): 1121. CrossRef

Original Paper

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.

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