GEO DATA

DATA ARTICLE

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; Received November 20, 2024 Accepted January 7, 2025 Published online February 13, 2025; DOI: https://doi.org/10.22761/GD.2024.0054 [Epub ahead of print]

77 View
10 Download

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.

Data Article

Assessment of the Usability of the Linkage between GLORIA and Sentinel-2 Imagery for the Surveillance of Algal Blooms in Freshwater Ecosystems: Gibeom Nam, Sunghwa Choi, Euiho Hwang, Kimook Kang, JinGyeom Kim, DongHyeon Yoon; GEO DATA. 2024;6(4):451-462. Published online December 31, 2024; DOI: https://doi.org/10.22761/GD.2024.0044

170 View
13 Download

Abstract PDF: Due to the recent anomalous climatic conditions, the mean summer temperature in Korea for the current year reached 25.6°C, marking the highest temperature recorded since 1973. Concurrently, the incidence of large-scale green algal blooms has escalated beyond the typical frequency in the four principal river systems. The Ministry of Environment has instituted a water quality monitoring network aimed at gathering data pertinent to water quality assessment and regulatory measures; however, the reliance on discrete point data imposes constraints on comprehending the spatial distribution and proliferation of green algae. Techniques for remote monitoring of green algae utilizing satellite imagery may serve as an alternative or complementary resource to conventional point-based water quality assessments. In the present study, we devised and critically assessed an algorithm for the remote estimation of freshwater chlorophyll-a concentrations through the analysis of Sentinel-2 satellite data. Given the challenges associated with field data collection, the GLORIA dataset was employed, and three variations of the CatBoost algorithm were developed based on different input variables, with their effectiveness concerning Sentinel-2 satellite data subjected to evaluation. Among the three algorithms, the variant that was trained utilizing variables such as band ratio and spectral shape was contrasted with 76 field measurements of surface chlorophyll-a, demonstrating a commendable accuracy with an R value of 0.79, root mean square error of 10.1 mg/m³ when applied to the GLORIA dataset. The chlorophyll-a quantification algorithm predicated on Sentinel-2 satellite data, which was developed in this study, holds the potential to be utilized effectively for collaborative management and responsive measures regarding green algae by providing objective insights into the prevalence of green algae across extensive areas, temporal trends, and localized hotspots.

Original Papers

Research on Building AI Learning Dataset for Synthetic Aperture Radar Waterbody Detection through Optical Satellite Image Fusion: Joonhyuk Choi, Ki-mook Kang, Euiho Hwang; GEO DATA. 2023;5(3):177-184. Published online September 27, 2023; DOI: https://doi.org/10.22761/GD.2023.0029

925 View
32 Download
1 Citations

Abstract PDF

For the spatiotemporal analysis of water resources and disasters, water body detection using satellite imagery is crucial. Recently, AI-based methods have been widely employed in water body detection using satellite imagery. To use these AI techniques, a substantial amount of training data is required. When creating training data for water body detection, optical imagery and synthetic aperture radar (SAR) imagery have their respective strengths and weaknesses. To use the advantages of both, this study proposes a water body detection method through the fusion of optical and SAR imagery. The results of the proposed model show an Intersection over Union of 0.612 and an F1 score of 0.759, which is better compared to using either optical or SAR imagery alone. This research presents a method that can easily generate a large amount of water body data, making it promising for use as AI training data for water body detection.

Citations

Citations to this article as recorded by

A Comprehensive Review of Remote Sensing for Water-Related Disaster Management in South Korea: Focus on Floods and Droughts
Eui-Ho Hwang, Jin-Gyeom Kim, Jang-Yong Sung, Ki-Mook Kang
Korean Journal of Remote Sensing.2024; 40(5-2): 833. CrossRef

Improvement of Algal Bloom Identification Using Satellite Images by the Algal Spatial Monitoring and Machine Learning Analysis in a New Dam Reservoir: Hye-Suk Yi, Sunghwa Choi, Dong-Kyun Kim, Hojoon Kim; GEO DATA. 2023;5(3):126-136. Published online September 25, 2023; DOI: https://doi.org/10.22761/GD.2023.0021

946 View
58 Download
1 Citations

Abstract PDF

Algal blooms are major issues and an ongoing cause of water quality problems in inland waters globally. In the case of harmful algal blooms, the water temperature rises after nitrogen and phosphorus inflow, which occurs in the summer, is the main cause of the algae bloom. In South Korea, algae monitoring methods have been performed by collecting water in point monitoring stations. Recently, in order to overcome the limitations of these existing monitoring methods, spatial monitoring methods using hyperspectral images and satellite images has been researched. We used satellite images for analysis of the spatial algal variation. The accuracy of algal identification is imperative for effective spatial monitoring of algal blooms in the context of ecological health and assessment. In this study, we generated algal big-data with simultaneously observed chlorophyll-a concentrations based on fluorescence measurement and predicted chlorophyll-a concentrations using 13- band satellite images derived from Sentinel-2. In order to validate the values from the satellite images, we compared them with simultaneously observed chlorophyll-a concentrations based on fluorescence measurement. The goal of this study is to improve the accuracy of predictions induced from satellite images. The analytical techniques were comparatively evaluated. The results showed that Artificial Neural Networks exhibited the best performance among them, improving more than 30% accuracy compared to that of multiple linear regression. Furthermore, the accuracy of identifying algal blooms has been shown to increase at high algal concentrations. In the end, it was successful to create algal bloom maps using a new algorithm to analyze algal bloom management.

Citations

Citations to this article as recorded by

Assessment of the Usability of the Linkage between GLORIA and Sentinel-2 Imagery for the Surveillance of Algal Blooms in Freshwater Ecosystems
Gibeom Nam, Sunghwa Choi, Euiho Hwang, Kimook Kang, JinGyeom Kim, DongHyeon Yoon
GEO DATA.2024; 6(4): 451. CrossRef

Articles

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

1,540 View
61 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.

Expand and Renewal of Analyzed Satellite Image and Service: Young-Woong Yoon, Che-Won Park, Sung-Hyun Gong, Won-Kyung Baek, Hyung-Sup Jung; GEO DATA. 2021;3(4):32-48. Published online December 31, 2021; DOI: https://doi.org/10.22761/DJ2021.3.4.005

1,286 View
30 Download

Abstract PDF: In this study, additional satellite analysis data in 2019, 2020, and 2021 were generated using Landsat-8 and Sentinel-2 satellite images. We additionally employed 19 types of satellite analysis methods, and generated totally 57 cases of satellite analysis data for three years. In addition, 34 types of satellite analysis data were updated using 2021 satellite data. In conclusion, a total of 91 cases of satellite analysis data were generated. The coverage of the study is the entire South Korea. The spatial resolution and the coordinate system were 30 m and UTM-K (EPSG: 5179) respectively. The products are provided as the entire South Korean and regional data, respectively. In addition, it is provided in three data types: ASCII, ArcGIS Grid, and GeoTIFF as same as last distribution. All satellite image analysis data can be downloaded free of charge from the Environmental Big Data website (www.bigdata-environment.kr), an environmental business big data platform.

Construction of Analyzed Satellite Image and Service: Won-Kyung Baek, Sung-Hwan Park, Jin-Woo Yu, Young-Woong Yoon, Hyung-Sup Jung; GEO DATA. 2020;2(2):45-55. Published online December 30, 2020; DOI: https://doi.org/10.22761/DJ2020.2.2.007

1,293 View
23 Download
1 Citations

Abstract PDF

In this study, 17 types of satellite analysis maps were generated using Landsat-8 and Sentinel-2 satellite images acquired at 2019 and 2020. Totally, 68 of satellite analysis data were produced. The scope of deployment is South Korea as a whole, with a resolution of 30 meters, and the coordinate system is UTM-K coordinates. The established data will be provided in both South Korean and regional data respectively. In addition, it is provided by three data format: ASCII, ArcGIS Grid, and GeoTIFF for enhancing accessibility of the data. All these satellite analysis data can be downloaded free of charge from the Environmental Big Data website (www.bigdata-environment.kr), an environmental business big data platform.

Citations

Citations to this article as recorded by

Expand and Renewal of Analyzed Satellite Image and Service
Young-Woong Yoon, Che-Won Park, Sung-Hyun Gong, Won-Kyung Baek, Hyung-Sup Jung
GEO DATA.2021; 3(4): 32. CrossRef

First
Prev
Page of 1
Next
Last

Search