GEO DATA

Data Article

Flood Susceptibility Map Data of Jeju Island Using Probabilistic Methods: Saro Lee; Received May 12, 2025 Accepted May 27, 2025 Published online June 11, 2025; DOI: https://doi.org/10.22761/GD.2025.0012 [Epub ahead of print]

49 View
2 Download

Abstract PDF: Floods are among the most widespread and destructive natural disasters globally, and Jeju Island in South Korea is particularly vulnerable due to its unique geological and hydrological characteristics. This study aims to produce a flood susceptibility map data for Jeju Island using a probabilistic method known as the frequency ratio (FR) model. The research integrates diverse spatial datasets including topography, geology, soil properties, land use, vegetation, and rainfall to analyze their correlation with historical flood occurrences. A flood inventory was constructed from multiple sources such as satellite images and disaster records. These data were used to train and validate the FR model. The model’s predictive performance was evaluated using the receiver operating characteristic-area under curve (ROC-AUC), achieving an accuracy 82.33%, indicating high accuracy. The resulting map data offers valuable insights for disaster mitigation, urban planning, and climate adaptation, especially in geologically complex regions like Jeju Island.

Original Paper

Vegetation Spatial Distribution on Taean Duung Wetland Protect Area: Haeseon Shin, Sanghun Lee, Sangwook Han; GEO DATA. 2024;6(1):8-13. Published online March 28, 2024; DOI: https://doi.org/10.22761/GD.2024.0004

1,390 View
88 Download
1 Citations

In this study, we conduct for providing information on the status of vegetation space distribution in the Duung wetland protected area and to help manage the wetland protected area. To understand the spatial distribution of vegetation in Duung Wetland, used the results of surveys in 2019 and 2023. As a result of the study, the number of vegetation types increased by 4 from 20 to 24. Four communities were newly investigated, including the Utricularia tenuicaulis community, Pueraria montana var. lobata-Elymus tsukushiensis community, Spiraea prunifolia for. simpliciflora community, and Miscanthus sinensis var. purpurascens community. In accordance with the environment, the range of aquatic plant communities such as Trapa japonica community and Nymphaea tetragona var. angusta community increased, and the succession zone of cultivated land expanded dry grassland. The survey results can be used as basic data for systematic management of the Duung wetland protected area.

Citations

Citations to this article as recorded by

Improving Inland Wetland Classification Performance of Drone Imagery-Based TransUNet Model Using Multi-Class Data Balancing Technique
Eu-Ru Lee, Jin-Sik Bong, Kyu-Ri Choi, Hyung-Sup Jung
Korean Journal of Remote Sensing.2025; 41(2): 447. CrossRef

Review Paper

Global Geospatial Data for Flood and Landslide Susceptibility Mapping: Saro Lee, Rezaie Fatemeh; GEO DATA. 2023;5(4):380-393. Published online December 28, 2023; DOI: https://doi.org/10.22761/GD.2023.0058

1,669 View
148 Download

Abstract PDF: Susceptibility mapping is an important component of natural hazard risk assessment and management. Susceptibility maps for floods and landslides, which are particularly damaging to human life and property, can provide a comprehensive understanding of risk areas and factors related to flood and landslide susceptibility. To create a global flood and landslide susceptibility map, global geospatial data for 37,984 landslide and 6,682 flood locations, as well as 11 selected environmental factors were used to construct a geographic information system database. The 11 environmental factors found to influence flood and landslide occurrence were rainfall, slope, terrain position index, plane curvature, terrain wetness index, distance from rivers, land use, soil texture, soil moisture, geology, and temperature. These data were then used directly to create a global flood and landslide susceptibility map.

Original Papers

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

1,715 View
76 Download
1 Citations

Abstract 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.

Citations

Citations to this article as recorded by

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

Construction of Exploration Data for Greenhouse Gas Geologic Storage: Focusing on Geological Cross-section Data: Bokyun Ko, Sungjae Park, Saro Lee; GEO DATA. 2023;5(3):222-229. Published online September 26, 2023; DOI: https://doi.org/10.22761/GD.2023.0023

1,316 View
108 Download

Abstract PDF: In this study, the most basic data, underground geological structure data, that is, geological cross-section data, were established to select a candidate site for underground storage of greenhouse gases based on AI. As a target area, the Gyeongsang Basin, where a large amount of sedimentary rocks are distributed, was selected as the greenhouse gas can be stored most effectively in sedimentary rocks. To this end, the acquisition and edit step, the refinement step, and the labeling step were carried out in the order of raw data collection, source data and labeling data construction to construct the geological cross-section data. This data can be downloaded through the AI hub site (https://aihub.or.kr/aihubdata/data/view.do?curr Menu=115&topMenu=100&aihubDataSe=realm&dataSetSn=71390) operated by the Korea Institute for Intelligent Information Society Promotion.

Long-Term Change and Analysis of the Sedimentary Environment of Dadae Beach Using Unmanned Aerial Vehicles: Joo Bong Jeong; GEO DATA. 2023;5(2):110-117. Published online June 30, 2023; DOI: https://doi.org/10.22761/GD.2023.0018

1,348 View
53 Download

Abstract PDF: Dadae Beach, located at the Nakdong river estuary, has been continuously evolving over the years, and this is the result of complex interactions between natural and artificial factors. In particular, in the case of Dadae Beach, located at the estuary of the Nakdong river estuary, it is located at the boundary between the river and the ocean, and it is an environment in which various deposition mechanisms operate. It is a very difficult research task to analyze the beach evolution mechanism, and a long-term study using precise measurement methods is required. Therefore, in this study, precision unmanned aerial surveys were conducted three times (2015, 2019, and 2021) for 5 years to identify the sedimentary characteristics of Dadae Beach, and the sedimentary environment was analyzed through the analysis of surface sediment texture characteristics. Seasonal waves and winds caused by the East Asian monsoon climate are the main mechanisms for the sedimentation of Dadae Beach, and finegrained sediments are distributed throughout the beach. In addition, the formation of sandbar, which arose rapidly due to artificial influences such as the construction of estuary banks in the past, is a major factor in the evolution of large-scale beaches. This study is meaningful in identifying the mechanism of beach evolution and presenting quantitative analysis results through comparison of precision aerial survey data over a long period of time.

Article

Spatial Distribution Characteristics of Seagrass Habitat Based on Remote Sensing Data: Focusing on Wan Island: Jung Yoon Yeo, Joo Bong Jeong, Jong Kyu Kim; GEO DATA. 2022;4(2):23-36. Published online June 30, 2022; DOI: https://doi.org/10.22761/DJ2022.4.2.003

1,814 View
69 Download

Abstract PDF: In May 2019, UAV photogrammetry using drones (unmanned aircraft) was conducted to investigate the spatial distribution characteristics of the seagrass habitat in Wan Island. Wan Is. sea was divided into 3 geographical areas (Site A, B, C) by referring to the seagrass distribution identified by the National Coastal seagrass forest precision survey (Ministry of Oceans and Fisheries, 2015), and contour lines were extracted from grid depth data. In addition, a 3-D benthic topographic map using DTM (Digital Terrain Model) was created to understand the characteristics and slope of the benthic topographic map. Through the analysis of the seagrass distribution results and the water depth results, it was confirmed that the boundary between the seagrass distribution in the outer and coastal areas converges to different water depth limits (within 10 m, and within 5 m), which is estimated to be based on the characteristics of the outer sea area. As a result of the analysis of the benthic topography, it was confirmed that the slope of the well-covered distribution was relatively gentle below 0.2~0.8 degrees, and the well-covered distribution was limited to the vicinity of the boundary where the slope changed rapidly. As a result of comparing the area of the seagrass distribution survey in 2015 and 2019, it was confirmed that the area was 238.4 ha and 1,070.9 ha, respectively, which was 4.5 times more widerly when performing Drone survey than diving survey. In order to understand the surrounding environment of the seagrass distribution site, satellite image data and license fishing ground information map at the time of drone shooting (May 2019) were checked. As a result, seaweed (Kelp, Sea mustard seaweed, Seaweed, Seaweed fulvescens, etc.) and shellfish (Abalone) were densely distributed, and most of the facilities except for Seaweed fulvescens farms were separated from their habitat. Drone survey confirmed Zostera marina, Zostera caulescens, and Zostera japonica in Wan Is., but no Phyllospadix iwatensis and Halophila nipponica were identified in 2015. It was confirmed that there was a limit to classifying the types of vegetation due to the characteristics of Phyllospadix iwatensis attached to the rock substrate, and in the case of Halophila nipponica with short leaves of 2 to 3 cm, they were not exposed to the water even at low tide, so there was a limit to detection using drones. These research results are expected to be useful data for grasping the characteristics of a wide range of seagrass habitats in other seas in the future.

Review

Data used for GIS-based Flood Susceptibility Mapping: Saro Lee, Fatemeh Rezaie; GEO DATA. 2022;4(1):1-15. Published online March 31, 2022; DOI: https://doi.org/10.22761/DJ2022.4.1.001

3,083 View
188 Download
7 Citations

Abstract PDF

The dramatic increase in flood incidents as a significant threat to human life and property, environment, and infrastructure indicates the necessity of mapping spatial distribution of flood susceptible areas to reduce destructive effects of flooding. During the last decade, the integration of the geographic information system (GIS) with the remote sensing data provide efficient means to generate a more reliable and precise flood susceptibility map. The present study contains a review of 200 articles on the application of GIS-based methods in indicating flood vulnerable areas. The papers were reviewed in terms of influential variables, study area, and the number of articles published in the last 10 years. The review shows that the number of studies has increased since 2012. The total study areas covered 39 countries that were mostly located in Asia where the major developments and infrastructures have been constructed in the floodplains. The most common study areas was Iran (44 articles, 22%), followed by India (26 articles, 13%), China (26 articles, 11%), and Vietnam (15 articles, 7.5%). More than 90 variables were considered to map flood susceptible areas that the top 5 widely used flood conditioning factor are slope (98% of total articles), followed by elevation (92% of total articles), land use/land cover (79.5% of total articles), distance to the river (76.5% of total articles), and rainfall (73% of total articles). The review implies that many natural and anthropogenic factors affect flooding and the combination of both groups of factors is necessary to accurately detect and map flood-prone parts of the study area.

Citations

Citations to this article as recorded by

Assessment of flood susceptibility in Cachar district of Assam, India using GIS-based multi-criteria decision-making and analytical hierarchy process
Preeti Barsha Borah, Arpana Handique, Chandra Kumar Dutta, Diram Bori, Shukla Acharjee, Lanusashi Longkumer
Natural Hazards.2025; 121(6): 7625. CrossRef
Mapping flood resilience: a comprehensive geospatial insight into regional vulnerabilities
Tauheed Ullah Khan, Ghulam Nabi, Sana Ullah, Ali Akbar, James Kehinde Omifolaji, Jahangir Khan Achakzai, Arshad Iqbal
Frontiers in Water.2025;[Epub] CrossRef
Integrating Machine Learning and Geospatial Data for Mapping Socioeconomic Vulnerability to Urban Natural Hazard
Esaie Dufitimana, Paterne Gahungu, Ernest Uwayezu, Emmy Mugisha, Jean Pierre Bizimana
ISPRS International Journal of Geo-Information.2025; 14(4): 161. CrossRef
Evaluating flood hazard risks using multicriteria analytical hierarchy process and rice farmers’ risk perceptions in Bharathapuzha River Basin, Kerala, South India
Dhanya Punnoli, Jayarajan Kunnampalli, Sreeraj Punnoli, Suresh Selvaraj
Environmental Science and Pollution Research.2025;[Epub] CrossRef
Taşkın Duyarlılığının Best-Worst Yöntemiyle Değerlendirilmesi: Suluca Deresi Havzası (Tokat) Örneği
Kemal Ersayın, Mustafa Yaş
Black Sea Journal of Engineering and Science.2024; 7(4): 683. CrossRef
Geo-Spatial Based Cyclone Shelter Suitability Assessment Using Analytical Hierarchy Process (AHP) in the Coastal Region of Bangladesh
Irteja Hasan, Md. Omar Faruk, Zarin Tasnim Katha, Md. Osman Goni, Md Shafiqul Islam, Tapas Ranjan Chakraborty, Sheikh Fahim Faysal Sowrav, Md Shakhawat Hossain
Heliyon.2024; : e39831. CrossRef
A systematic flood risk assessment of Bloemfontein Watershed, South Africa
Zachariah H. Mshelia, Johanes A. Belle
Geomatics, Natural Hazards and Risk.2024;[Epub] CrossRef

Articles

Digital Coal Geological Map: Saro Lee; GEO DATA. 2021;3(3):1-7. Published online September 30, 2021; DOI: https://doi.org/10.22761/DJ2021.3.3.001

1,304 View
103 Download

Abstract PDF: From 1974 to 1994, the Korea Institute of Geoscience and Mineral Resources (KIGAM) systematically prepared and published relatively precise coal cell geology maps of 1:10,000 or 1:25,000 scale for major coal fields across the country. Such a coal cell geology map includes information about the coal seams as well as the geology of the coal field area, so it can be used as an important basic data for coal development. In this paper, the current state of the coal geology map, which was digitized into a spatial DB using GIS, was introduced. This digital coal geological map can be downloaded free of charge from the Geo-Big Data Open Platform (data.kigam.re.kr) and the Environmental Big Data Platform (www.bigdata-environment.kr).

Mapping of Groundwater Pollution Vulnerability in Entire South Korea Using DRATIC Model: Won-Kyung Baek, Sung-Hwan Park, Jin-Woo Yu, Young-Woong Yoon, Hyung-Sup Jeong; GEO DATA. 2021;3(2):32-38. Published online July 21, 2021; DOI: https://doi.org/10.22761/DJ2021.3.2.005

1,713 View
89 Download

Abstract PDF: Groundwater pollution vulnerability was mapped for entire South Korea using groundwater, topography, geology, and soil data. For this, the DRASTIC model developed by the US Environmental Protection Agency was used and the geographic information system (GIS) was used as the basic tool. This groundwater pollution vulnerability map can be usefully used as basic data for groundwater development and conservation management. The constructed data is provided as entire South Korean and regional data, respectively. In addition, in order to expand the accessibility of the data, it is converted and provided in three data formats: ASCII, ArcGIS Grid, and GeoTIFF. All these satellite image analysis data can be downloaded free of charge from the Environment Big Data Platform website (www.bigdata-environment.kr).

Geothermal Data of Chungcheong-do: Jae-Hong Hwang, Hyeong-Chan Kim; GEO DATA. 2019;1(1):46-49. Published online December 30, 2019; DOI: https://doi.org/10.22761/DJ2019.01.01.007

639 View
13 Download

Abstract PDF: Preexistence geothermal representation technology has been used to build a GIS database for accurate geothermal information. Based on this, Chungcheong-do was analyzed. GIS composition and statistical analysis of Chungcheong-do were established and regional targets were calculated. Geothermal representation was performed through a large number of borehole data. Rock characterization of Chungcheong-do was performed using borehole data and rock samples. Therefore, we attempted to summarize the geothermal statistics and create a geothermal theme map for regional analysis and evaluation of geothermal characteristics of Chungcheong-do.

First
Prev
Page of 1
Next
Last

Search