GEO DATA

Data Articles

Constructing Mammal Occurrence Status and Deep Learning Training Data from Camera Trap Surveys in Wind Farm: Dong-Joo Kim, Do-Hun Lee, Nanghee Kim, Jun-Haeng Heo, Yong Sung Kwon; GEO DATA. 2025;7(4):559-567. Published online December 31, 2025; DOI: https://doi.org/10.22761/GD.2025.0074

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Abstract PDF: In South Korea, there has been an increase in the development of onshore wind farms as part of national strategies to achieve carbon neutrality. However, concerns have been raised regarding the potential ecological impacts on terrestrial mammals posed by the placement of these facilities in forested mountain areas. In order to establish a baseline for the purpose of impact assessment, camera trap surveys were conducted at two onshore wind farms - Gasan (GS) and Moochang (MC) - and differences in community composition and spatial activity patterns were examined. A total of 40 camera traps were deployed, yielding 1,875 usable images between May and December 2023 (excluding legally protected species). Eleven species from eight families were documented. GS exhibited a relatively even assemblage, dominated by Cervidae (31.6%), Mustelidae (28.0%), and Sciuridae (14.5%), whereas MC showed lower evenness and strong dominance by Mustelidae (29.0%), Cervidae (28.6%), and Suidae (21.2%). Spatial distribution mapping indicated that GS detections were concentrated along boundary ridgelines, while MC detections clustered near access roads and lower slopes. This reflects different habitat-use patterns under wind farm development. During the data processing stage, a significant amount of effort was required for the manual annotation and formatting of the data. In order to streamline this process, a user-assisted labeling tool was developed that automates metadata extraction and produces both monitoring-ready datasets and deep learning-compatible annotations. While not directly related to the ecological objective, this workflow exemplifies a pragmatic approach to reducing processing demands and paving the way for future AI-driven analyses. This study provides one of the first standardized datasets of mammal occurrence for Korean wind farm sites, and highlights the necessity for longterm, repeatable monitoring supported by scalable data-management frameworks.

An ECDF-based Method for Generating DEM Training Data Simulating Sub-waterbody Elevation: ChanHee Jeong, Nayeon Kim, Gibeom Nam, Kimook Kang, Euiho Hwang, DongHyeon Yoon; GEO DATA. 2025;7(4):811-823. Published online December 29, 2025; DOI: https://doi.org/10.22761/GD.2025.0049

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Abstract PDF: Satellite-derived digital elevation models (DEMs) are indispensable for hydrologic analysis, yet riverbed topography beneath water bodies is systematically missing because of water reflectance/dispersion and water-level variability between satellite revisits. To address this structural limitation, we propose an automatic dataset construction method for DEM inpainting. For each DEM patch, the empirical cumulative distribution function of elevations is compared with a linear baseline; the threshold is chosen at the knee point where their vertical deviation is maximized. Elevations at or below this threshold are masked as likely water-occupied terrain, while the original DEM serves as ground truth. Using the Copernicus DEM GLO-30, we sample patches from 30°S to 30°N, enforce a water-area ratio of 5-15%, and apply morphological post-processing. A patch size of 64×64 is adopted, yielding 10,000 input-ground-truth pairs in GeoTIFF format (EPSG:4326). Dataset validity is assessed with a lightweight multi-scale U-Net, evaluation is restricted to the masked (missing) regions. On the test set, the model achieves PSNR 13.44 dB, SSIM 0.78, and MAE 2.80 m. Qualitative analyses show stable reconstruction of topographic continuity near shorelines, with most vertical errors confined to within ±5 m around boundaries. The proposed approach enables standardized, automated generation of large training datasets that reflect local terrain characteristics without field-grade ground truth, providing a practical basis for hydrologically complete DEMs for riverbed restoration, water-level estimation, and large-scale hydrologic simulation. Future work will extend to mid- and high-latitude regions and incorporate hydrological-consistency constraints to jointly improve structural similarity and absolute accuracy.

WorldView-3 Super-resolution Training Dataset Construction Using the MTF-GLP Method: Nayeon Kim, ChanHee Jeong, Gibeom Nam, Kimook Kang, Euiho Hwang, DongHyeon Yoon; GEO DATA. 2025;7(4):835-848. Published online December 23, 2025; DOI: https://doi.org/10.22761/GD.2025.0045

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Abstract PDF: The demand for high-resolution satellite imagery is rapidly increasing in various fields such as national defense, disaster response, and environmental monitoring, drawing significant attention to super-resolution (SR) techniques in remote sensing. In this study, a high-quality high resolution (HR)-low resolution (LR) training dataset was generated using a single sub-meter satellite image acquired from WorldView-3. The enhanced correlation coefficient image registration method was employed to correct geometric distortions between the panchromatic and multispectral images, and the modulation transfer function-generalized Laplacian pyramid algorithm was applied to produce HR images with improved spatial resolution while preserving spectral fidelity. LR images were synthesized through sensor-specific degradation modeling, resulting in a total of 14,776 HR-LR patch pairs. The constructed dataset covers diverse land cover types, including urban areas, agricultural fields, and water bodies, enabling comprehensive evaluation of super-resolution models in both spatial detail reconstruction and spectral information preservation. Unlike conventional datasets that rely on cross-sensor fusion with complex preprocessing, this study simplifies the data generation pipeline using a single-sensor approach and ensures spatial-spectral consistency. The proposed dataset is expected to serve as a reliable benchmark for training and evaluating deep learning-based satellite image SR models.

GeoAI Dataset for Building Change Detection from CAS500-1: DongHyuk Jin, Ji Sang Park, Yuna Jang, Seungha Lee, Junhwa Chi; GEO DATA. 2025;7(3):216-229. Published online September 2, 2025; DOI: https://doi.org/10.22761/GD.2025.0010

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Abstract PDF: Building change detection (BCD) is essential for urban planning, disaster response, and environmental monitoring. Existing datasets, such as WHU-CD, LEVIR-CD, and SYSUCD, as well as overseas satellite imagery datasets, have limitations in reflecting South Korea’s distinctive high-density urban morphology. Domestic aerial datasets, including AI-based national park change monitoring and urban BCD datasets, also suffer from varying sensor resolutions and limited applicability to satellite-based monitoring. To overcome these challenges, we introduce a high-resolution BCD dataset based on Compact Advanced Satellite 500-1 (CAS500-1), launched in 2021. This dataset utilized 0.5 m/pixel satellite imagery collected from 2021 to 2024 across diverse regions, including Seoul, Busan, and Daejeon. The dataset construction involved precise image alignment, RGB band synthesis, histogram stretching for contrast enhancement, and polygon-based labeling to identify urban changes. Additionally, format conversions were applied to ensure deep learning compatibility, generating 3,803 paired image patches (256×256 pixels). To validate the dataset, we employed ChangeMamba, a model integrating a visual state space model for spatiotemporal change detection. The model trained on our dataset achieved an F1 score of 0.79 and an intersection over union (IoU) of 0.66, outperforming models trained on different BCD datasets in cross-dataset tests, where the latter scored significantly lower (e.g., F1, 0.18 and IoU, 0.10 for the model trained on LEVIR-CD). Moreover, our dataset demonstrated improved consistency over domestic aerial datasets, which often suffer from heterogeneous resolutions and limited scalability for long-term monitoring. By leveraging high-resolution satellite imagery, our dataset provides a robust alternative to both overseas and domestic aerial datasets, enabling precise urban monitoring with reduced reliance on aerial surveys. It serves as a critical resource for advancing BCD research in South Korea. Future research will focus on automating the labeling process, expanding temporal coverage, and improving model performance through reinforcement learning on pre-trained models using this dataset.

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; Correction in: GEO DATA 2025;7(2):120

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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.

GeoAI Dataset for Urbanized Area Segmentation from Landsat 8/9 Satellite Imagery and GEMS: Sung-Hyun Gong, Hyung-Sup Jung, Geun-Han Kim, Geun-Hyouk Han, Il-Hoon Choi, Jin-Sung Hong; GEO DATA. 2024;6(4):478-486. Published online December 31, 2024; DOI: https://doi.org/10.22761/GD.2024.0053; Correction in: GEO DATA 2025;7(2):118

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In South Korea, air pollution has emerged as a pressing social issue, necessitating data-driven approaches to monitor sources of air pollutants. This study constructed a GEO AI dataset for detecting air pollution sources in urbanized areas, utilizing Landsat 8/9 satellite imagery, Geostationary Environment Monitoring Spectrometer geostationary satellite data, and air quality monitoring network data. The dataset is optimized for semantic segmentation tasks, including labeled data for urban area segmentation, and is designed to enable precise detection of pollution sources within urban regions by integrating satellite imagery and air quality information. Using this dataset, we applied a modified U-Net model to classify pollutant sources in urbanized areas, achieving high performance with an mIoU of 0.8592 and pixel accuracy of 0.9433. These results demonstrate the effectiveness of the GEO AI dataset as a tool for identifying and managing major pollution sources, providing foundational data for air quality monitoring and policy development across South Korea and East Asia. With further integration of additional air pollution data, this dataset is expected to contribute to long-term air quality management and the mitigation of health impacts associated with pollution.

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Semantic Segmentation of Urbanized Areas Using Multi-Encoder U-Net Based on Multi-Modal Data
Sung-Hyun Gong, Hyung-Sup Jung, Geun-Han Kim, Geun-Hyouk Han, Il-Hoon Choi, Jin-Sung Hong
Korean Journal of Remote Sensing.2025; 41(2): 461. CrossRef
Sub-Pixel Classification of Water Body from Sentinel-1 SAR Images Using Attention V-net
Jun-Hyeok Jung, Jin-Woo Yu, Hyung-Sup Jung
Korean Journal of Remote Sensing.2025; 41(5): 769. CrossRef

Solar Energy Datasets of Deep Learning Models Incorporating with GK-2A and ASOS Ground Measurements: Jong-Sung Ha, Seungtaek Jeong, Seyun Min, Yejin Lee, Suhwan Kim, Doehee Han, Jong-Min Yeom; GEO DATA. 2024;6(4):471-477. Published online December 31, 2024; DOI: https://doi.org/10.22761/GD.2024.0036

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Abstract PDF: This study presents the construction and evaluation of a dataset for estimating solar energy using the GK^-2A satellite and deep learning. The GK^-2A is currently utilized in real-time for weather observations over the Korean Peninsula. The GK^-2A satellite features 16 channels, producing radiative channel images at spatial resolutions ranging from 500 m to 2 km, with temporal intervals as short as 2 minutes depending on the area. These satellite data are used in various fields, including meteorology, oceanography, vegetation monitoring, and renewable energy. In this study, we used spectral channel data from the GK^-2A expended local area satellite from January 2021 to December 2022. For training and evaluating the accuracy of the deep learning model, we utilized data from 98 automated synoptic observing system ground observation sites operated by the Korea Meteorological Administration. A back-propagation neural network model, which showed meaningful results in estimating solar energy, was applied. Various hyperparameters were optimized, and data preprocessing and separation were conducted to optimize the model. The study also compared the performance of the deep learning model with physical models. The BPNN deep learning model achieved a statistical accuracy of root mean squared error (RMSE) 77.32 Wm^-2, mean bias error (MBE) -0.48 Wm^-2, and R² 0.91, indicating high accuracy. In contrast, the physical model showed an RMSE of 132.01 Wm^-2, MBE -76.51 Wm^-2, and an R² of 0.74, displaying relatively lower accuracy compared to the deep learning model. Additionally, the spatio-temporal map of solar energy generated by the deep learning model successfully captured the attenuation of radiation due to clouds and the variation in solar energy based on the position of the sun. The solar energy data produced in this study are expected to be useful as input data for various fields such as meteorology, agriculture, environmental monitoring, and marine sciences.

Original Papers

GeoAI Dataset for Rural Hazardous Facilities Segmentation from KOMPSAT Ortho Mosaic Imagery: Sung-Hyun Gong, Hyung-Sup Jung, Moung-Jin Lee, Kwang-Jae Lee, Kwan-Young Oh, Jae-Young Chang; GEO DATA. 2023;5(4):231-237. Published online December 28, 2023; DOI: https://doi.org/10.22761/GD.2023.0054

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In South Korea, rural areas have been recognized for their potential as sustainable spaces for the future, but they are currently facing major problems. Unplanned construction of facilities such as factories, livestock facilities, and solar panels near residential areas is destroying the rural environment and deteriorating the quality of life of residents. Detection and monitoring of rural facilities are necessary to prevent disorderly development in rural areas and to manage rural space in a planned manner. In this study, satellite imagery data was utilized to obtain information on rural areas, which is useful for observing large areas and monitoring time series changes compared to field surveys. In this study, KOMPSAT ortho-mosaic optical imagery from 2019 and 2020 were utilized to construct AI training datasets for rural hazardous facilities segmentation for Seosan, Anseong, Naju, and Geochang areas. The dataset can be used in image segmentation models to classify rural facilities and can be used to monitor potentially hazardous facilities in rural areas. It is expected to contribute to solving rural problems by serving as the basis for rural planning.

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Performance Comparison of Water Body Detection from Sentinel-1 SAR and Sentinel-2 Optical Imagery Using Attention U-Net Model
Il-Hoon Choi, Eu-Ru Lee, Hyung-Sup Jung
Korean Journal of Remote Sensing.2024; 40(5-1): 507. CrossRef

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

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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.

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Tracing the evolution of urban flood vulnerability: assessing the impact of subsurface drainage upgrades in Seoul using deep learning
Junhyeok Jung, Euru Lee, Hyung-Sup Jung
Geosciences Journal.2026;[Epub] CrossRef
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. CrossRef
Semantic Segmentation of Urbanized Areas Using Multi-Encoder U-Net Based on Multi-Modal Data
Sung-Hyun Gong, Hyung-Sup Jung, Geun-Han Kim, Geun-Hyouk Han, Il-Hoon Choi, Jin-Sung Hong
Korean Journal of Remote Sensing.2025; 41(2): 461. CrossRef
Performance Comparison of Water Body Detection from Sentinel-1 SAR and Sentinel-2 Optical Imagery Using Attention U-Net Model
Il-Hoon Choi, Eu-Ru Lee, Hyung-Sup Jung
Korean Journal of Remote Sensing.2024; 40(5-1): 507. CrossRef

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

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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.

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Center Point Matching Network Based on Multi-Coverage Templates for High-Resolution Satellite Orthorectification
Jaewon Hur, Junseo Baek, Doochun Seo, Changno Lee, Jaehong Oh, Jaewan Choi, Youkyung Han
KSCE Journal of Civil Engineering.2026; : 100584. CrossRef
WorldView-3 Super-resolution Training Dataset Construction Using the MTF-GLP Method
Nayeon Kim, ChanHee Jeong, Gibeom Nam, Kimook Kang, Euiho Hwang, DongHyeon Yoon
GEO DATA.2025; 7(4): 835. CrossRef
Performance Comparison of Water Body Detection from Sentinel-1 SAR and Sentinel-2 Optical Imagery Using Attention U-Net Model
Il-Hoon Choi, Eu-Ru Lee, Hyung-Sup Jung
Korean Journal of Remote Sensing.2024; 40(5-1): 507. CrossRef

Comparative Study of Machine Learning and Deep Learning Models Applied to Data Preprocessing Methods for Dam Inflow Prediction: Youngsik Jo, Kwansue Jung; GEO DATA. 2023;5(2):92-102. Published online June 30, 2023; DOI: https://doi.org/10.22761/GD.2023.0016

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In this study, we employed representative machine learning (ML) and deep learning (DL) models previously utilized in the fields of rainfall and runoff analysis in the water resources sector. We not only performed hyperparameter tuning of the models but also considered the characteristics of the model and the combination and preprocessing (such as lag-time and moving average) of meteorological and hydrological data. We then compared and evaluated the performance of the models according to various scenarios of data characteristics and ML & DL model combinations for predicting daily water inflow. To accomplish this, we utilized meteorological and hydrological data collected from 1974 to 2021 in the Soyang River Dam Basin to examine 1) precipitation, 2) inflow, and 3) meteorological data as primary independent variables. We then employed a total of 36 scenario combinations as input data for ML & DL, applying a) lag-time, b) moving average, and c) component separation conditions for inflow. To identify the most suitable data combination characteristics and ML & DL models for predicting daily inflow, we compared and evaluated 10 different ML & DL models: 1) Linear Regression, 2) Lasso, 3) Ridge, 4) Support Vector Regression, 5) Random Forest (RF), 6) Light Gradient Boosting Model, 7) XGBoost for ML, and 8) Long Short-Term Memory (LSTM) models, 9) Temporal Convolutional Network (TCN), and 10) LSTM-TCN for DL.

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Machine learning-enhanced baseflow estimation from digital separation methods: insights from south-western Nigeria
Ayoola Emmanuel Awode, James Rotimi Adewumi, Obinna Obiora-Okeke, Akinola Adesuji Komolafe
Water Practice & Technology.2025; 20(10): 2173. CrossRef

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