- 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
-
-
 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
-
-
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
-
-
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.
|
GAIDAS
