1석박사통합과정생, 서울시립대학교 공간정보공학과, 서울특별시 동대문구 서울시립대로 163, 02504, 대한민국
2석박사통합과정생, 서울시립대학교 스마트시티학과, 서울특별시 동대문구 서울시립대로 163, 02504, 대한민국
3석사, 서울시립대학교 공간정보공학과, 서울특별시 동대문구 서울시립대로 163, 02504, 대한민국
4석사, 서울시립대학교 스마트시티학과, 서울특별시 동대문구 서울시립대로 163, 02504, 대한민국
5연구관, 국립환경과학원 환경위성센터, 인천광역시 서구 환경로 42, 22689, 대한민국
6연구사, 국립환경과학원 환경위성센터, 인천광역시 서구 환경로 42, 22689, 대한민국
7연구원, 국립환경과학원 환경위성센터, 인천광역시 서구 환경로 42, 22689, 대한민국
8교수, 서울시립대학교 공간정보학과, 서울특별시 동대문구 서울시립대로 163, 02504, 대한민국
9교수, 서울시립대학교 스마트시티학과, 서울특별시 동대문구 서울시립대로 163, 02504, 대한민국
1Integrated Master and PhD Student, Department of Geoinformatics, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, 02504 Seoul, South Korea
2Integrated Master and PhD Student, Department of Smart Cities, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, 02504 Seoul, South Korea
3Master, Department of Geoinformatics, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, 02504 Seoul, South Korea
4Master, Department of Smart Cities, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, 02504 Seoul, South Korea
5Senior researcher, Environmental Satellite Center, National Institute of Environmental Research, 42 Hwangyeong-ro, Seo-gu, 22689 Incheon, South Korea
6Researcher, Environmental Satellite Center, National Institute of Environmental Research, 42 Hwangyeong-ro, Seo-gu, 22689 Incheon, South Korea
7Research Official, Environmental Satellite Center, National Institute of Environmental Research, 42 Hwangyeong-ro, Seo-gu, 22689 Incheon, South Korea
8Professor, Department of Geoinformatics, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, 02504 Seoul, South Korea
9Professor, Department of Smart Cities, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, 02504 Seoul, South Korea
Copyright © 2024 GeoAI Data Society
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Conflict of Interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Funding Information
This work was supported by National Institute of Environmental Research with the funding of the Ministry of Environment (NIER-2023-01-01-086).
Data Availability Statement
The data that support the findings of this study are openly available in DataON at https://doi.org/10.22711/idr/1031.
Sort | Field | Subcategory#1 | Subcategory#2 |
---|---|---|---|
Essential | *Title of Dataset | Dataset for deep learning-based GEMS Asian dust detection | |
*DOI | https://doi.org/10.22711/idr/1031 | ||
*Category | utilitiesCommunication | ||
*Temporal Coverage | Deep Learning Based Asian Dust Segmentation Dataset: Input (6 Nomralized Radiances, UVAI, VISAI), Label (Asian Dust Annotation Data). | ||
The dataset is organized in the form of a python pickle file. | |||
*Spatial Coverage | WGS84 | ||
Latitude:30°-45° | |||
Longitude:100°-132° | |||
*Personnel | Name | Jin-Woo Yu | |
Affiliation | University of Seoul | ||
jinwooy@uos.ac.kr | |||
*CC License | CC BY-NC | ||
Summary of Dataset | AI training dataset for training a deep learning-based GEMS Asian dust detection model | ||
Optional | Project | Studies on artificial intelligence model development for improving Geostationary Environmental Monitoring Spectrometer (GEMS) output | |
Instrument |
Date of occurrence of Asian dust |
---|
January 12, 2021 |
January 13, 2021 |
January 14, 2021 |
January 15, 2021 |
March 27, 2021 |
March 28, 2021 |
March 29, 2021 |
March 30, 2021 |
April 15, 2021 |
April 16, 2021 |
April 17, 2021 |
April 26, 2021 |
April 27, 2021 |
April 28, 2021 |
April 29, 2021 |
May 6, 2021 |
May 7, 2021 |
May 8, 2021 |
May 22, 2021 |
May 23, 2021 |
Data | Data size | Data type | Resolution | Area |
---|---|---|---|---|
Input data | ||||
Norm radiance 1 | Width: 640 pixel | pkl | 5 km | Latitude: 30°-45° |
Norm radiance 2 | Height: 300 pixel | Longitude: 100°-132° | ||
Norm radiance 3 | Channel: 8 channel | |||
Norm radiance 4 | ||||
Norm radiance 5 | ||||
Norm radiance 6 | ||||
GEMS UVAI | ||||
GEMS VISAI | ||||
Label data | ||||
Labeled data about Asian dust | Width: 640 pixel | |||
Height: 300 pixel | ||||
Channel: 1 channel |
Sort | Field | Subcategory#1 | Subcategory#2 |
---|---|---|---|
Essential | *Title of Dataset | Dataset for deep learning-based GEMS Asian dust detection | |
*DOI | |||
*Category | utilitiesCommunication | ||
*Temporal Coverage | Deep Learning Based Asian Dust Segmentation Dataset: Input (6 Nomralized Radiances, UVAI, VISAI), Label (Asian Dust Annotation Data). | ||
The dataset is organized in the form of a python pickle file. | |||
*Spatial Coverage | WGS84 | ||
Latitude:30°-45° | |||
Longitude:100°-132° | |||
*Personnel | Name | Jin-Woo Yu | |
Affiliation | University of Seoul | ||
jinwooy@uos.ac.kr | |||
*CC License | CC BY-NC | ||
Summary of Dataset | AI training dataset for training a deep learning-based GEMS Asian dust detection model | ||
Optional | Project | Studies on artificial intelligence model development for improving Geostationary Environmental Monitoring Spectrometer (GEMS) output | |
Instrument |
GEMS, Geostationary Environment Monitoring Spectrometer; UVAI, ultraviolet-aerosol index; VISAI, visible-aerosol index.