GEO DATA

Articles

Construction of a Training Dataset for Vessel Distribution Prediction: The Northern Seas of Jeju Island: Yonggil Park, Taehoon Kim, Hyeon-Gyeong Han, Cholyoung Lee; GEO DATA. 2022;4(2):37-46. Published online June 30, 2022; DOI: https://doi.org/10.22761/DJ2022.4.2.004

595 View
38 Download

Abstract PDF: Recently, interest in maritime accidents and safety-related research, such as preventing collisions between marine vessels, detecting illegal vessels, and predicting vessel routes, is increasing. Vessel location data-based vessel distribution map can support decision-making for maritime safety management, and if the vessel distribution can be predicted, it is possible to take a preemptive response for maritime security such as fishing safety management and illegal fishing prevention. In this study, a training dataset for vessel distribution prediction was constructed by collecting V-Pass data, weather warnings, and marine environment data. The result of resampling of reporting interval of vessel location data was mapped to grid data to evaluate the vessel density, and a total of 1,314,000 of training data were constructed for the study area. In the future, research to evaluate the accuracy by performing vessel distribution prediction modeling should be conducted.

A Geological Environment Characteristics Dataset of Tidal Flat Surface Sediments: A 2021 Pilot Study of the Gomso Bay Tidal Flat Area to Use of Sediment Type Data: Kyoungkyu Park, Han Jun Woo, Hoi-Soo Jung, Joo Bong Jeong, Joo-Hyung Ryu, Jun-Ho Lee; GEO DATA. 2022;4(2):9-22. Published online June 30, 2022; DOI: https://doi.org/10.22761/DJ2022.4.2.002

973 View
31 Download
1 Citations

The Gomso Bay tidal flat is located between Buan-gun and Gochang-gun in Jeollabuk-do, Korea; it is a semi-closed bay in an area where tides prevail over waves. Tidal flats are mainly found south of Gochang-gun, and the main stream located north of the tidal flats is about 15 m deep and 900 m wide at low tide. Limited direct sampling is necessary for analyzing the geological environment of intertidal tidal flats, depending on the expected ebb-tide time and the number of survey items allowed for tidal flat access. This study assessed field measurement and laboratory analysis items for obtaining and establishing geological environment data to use of sediment type data in a pilot research area in the Gomso Bay tidal flat. Thirty sites were examined on June 22 and 24, 2021 (survey time about 3.5 hours for the 2 days). The field measurements were the sample date (year/month/day/hour/minute), ellipsoid height using a real-time kinematics global positioning system (RTK GPS) (m), shear strength (kg/cm²), and Munsell color. Samples for particle size (phi, Φ), specific density, porosity (%), moisture content (%), total organic carbon (%), total carbon (%) and total nitrogen (%) were placed in zipper bags and polypropylene (PP) bottles. The sedimentary phases were classified following Folk and Ward (1957), the organic matter was characterized based on particle size analysis and each experimental result was verified. In the future, a geological environment characteristics dataset based on this pilot study will be used as basic data to assess changes in the tidal flat topography and sedimentation environment. It should be useful data for research, tidal flat environment conservation management and free open data for users of related researchers.

Citations

Citations to this article as recorded by

Characteristics of temporal-spatial variations of zooplankton community in Gomso Bay in the Yellow Sea, South Korea
Young Seok Jeong, Min Ho Seo, Seo Yeol Choi, Seohwi Choo, Dong Young Kim, Sung-Hun Lee, Kyeong-Ho Han, Ho Young Soh
Environmental Biology Research.2023; 41(4): 720. CrossRef

AI Dataset for Road Detection using KOMPSAT Images: Hoonhee Lee, Han Oh; GEO DATA. 2022;4(1):43-48. Published online March 31, 2022; DOI: https://doi.org/10.22761/DJ2022.4.1.005

420 View
22 Download

Abstract PDF: Information on shape and type of road present in an optical image of satellite is useful for digital mapping and monitoring of road changes. Processing and structuring optical image data collected from payloads mounted on KOMPSAT 3 and 3A can accelerate the development of road detection algorithms and the extraction of road information using them. In particular, if it is built with a learning dataset for AI (Artificial Intelligence) prepared to apply deep learning technology, the latest artificial intelligence technology in the field of computer science can be spun off to the field of satellite image-based road detection to attempt a wide range of analysis. Korea Aerospace Research Institute constructed an image dataset for AI learning using satellite optical images with Korean companies, and this paper explains the type and size of datasets along with examples of the use of the dataset. The established data can be used through the website, aihub.or.kr.

Dataset for Water Body Detection Using Satellite SAR Images: SeungJae Lee, Han Oh; GEO DATA. 2021;3(2):12-19. Published online July 21, 2021; DOI: https://doi.org/10.22761/DJ2021.3.2.002

635 View
28 Download

Abstract PDF: Satellite synthetic aperture radar (SAR) generates valid image information in all-weather. Thus, it can be effectively used for near real-time monitoring and damage analysis of flood areas which always involve overcast skies. Water body detection (WBD) using SAR images can be implemented by various techniques which discriminate electromagnetic characteristics between water and non-water areas. Especially, semantic segmentation exploiting artificial intelligence techniques can be used to develop a high-performance WBD model. To this end, Korea Aerospace Research Institute has built an WBD dataset using KOMPSAT-5 images. The dataset is currently available through the website, aihub.or.kr.

AI Training Dataset for Cloud Detection of KOMPSAT Images: Bo-Ram Kim, Han Oh; GEO DATA. 2020;2(2):56-62. Published online December 30, 2020; DOI: https://doi.org/10.22761/DJ2020.2.2.008

515 View
29 Download

Abstract PDF: Clouds that appear inevitably when acquiring optical satellite images hinder the interpretation of surface information, so removing them is a crucial procedure to increase the utilization of satellite images. Currently, for KOMPSAT (Korea Multi-purpose Satellite) images, only the cloud amount by visual measurement is proved for the entire scene and detailed cloud masks are not provided. Since cloud detection is a time-consuming task, we built a cloud dataset for KOMPSAT images so as to develop an algorithm that expedites the task with state-of-the-art artificial intelligent techniques. In the dataset, satellite images were selected from various regions considering that clouds have different characteristics depending on the region, and masks were classified into thin clouds, thick clouds, cloud shadows, and clear sky. The size of dataset is over 4,000 image/mask pairs by an image size of 1000x1000 and one of the largest among publicly available cloud datasets, as of this writing. The dataset is built by a government AI (artificial intelligent) training dataset building program and will be available through the website, aihub.or.kr.

First
Prev
Page of 1
Next
Last

Search