GeoAI Dataset for Training a Deep Learning-based GEMS Snow Detection Model
The Geostationary Environment Monitoring Spectrometer (GEMS) observes air quality across East Asia from an altitude of approximately 36,000 km, analyzing the spatiotemporal distribution of atmospheric pollutants that spread beyond localized regions. GEMS currently provides 21 core air quality-relate...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
GeoAI Data Society
2024-12-01
|
| Series: | Geo Data |
| Subjects: | |
| Online Access: | http://geodata.kr/upload/pdf/GD-2024-0060.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849391611014807552 |
|---|---|
| author | Jin-Woo Yu Jun-Hyeok Jung Kyoung-Hee Kang Yong-Mi Lee Hyung-Sup Jung |
| author_facet | Jin-Woo Yu Jun-Hyeok Jung Kyoung-Hee Kang Yong-Mi Lee Hyung-Sup Jung |
| author_sort | Jin-Woo Yu |
| collection | DOAJ |
| description | The Geostationary Environment Monitoring Spectrometer (GEMS) observes air quality across East Asia from an altitude of approximately 36,000 km, analyzing the spatiotemporal distribution of atmospheric pollutants that spread beyond localized regions. GEMS currently provides 21 core air quality-related products, most of which are derived from Level 1C data, which has undergone geometric and radiometric correction. For enhanced accuracy in air quality analysis, precise surface reflectance estimation is essential. However, high-reflectance elements, such as snow, interfere with the accurate estimation of radiance values, necessitating precise detection of such areas. Despite this, GEMS relies solely on the ultraviolet and partial visible bands, lacking the infrared bands crucial for snow detection, and it has no proprietary snow detection algorithm, instead utilizing near-real-time ice and snow extent data from the U.S. National Snow and Ice Data Center. Recently, deep learning techniques have shown potential in image processing, outperforming traditional algorithms, which could address these limitations. However, there is currently no deep learning training dataset available for snow detection specifically for GEMS. To address this issue, this study developed a GeoAI dataset for training a deep learning-based snow detection model for GEMS. In this research, we constructed input data using GEMS Level 1C data and generated label data based on GEMS, Advanced Meteorological Imager, and MODIS snow cover data. The snow detection dataset developed in this study is expected to address the snow detection limitations of GEMS, providing foundational data to enhance the reliability of future geostationary satellite-based air quality research. |
| format | Article |
| id | doaj-art-2cdc84c17d0243248c0a161b6fc86e1a |
| institution | Kabale University |
| issn | 2713-5004 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | GeoAI Data Society |
| record_format | Article |
| series | Geo Data |
| spelling | doaj-art-2cdc84c17d0243248c0a161b6fc86e1a2025-08-20T03:41:00ZengGeoAI Data SocietyGeo Data2713-50042024-12-016455256010.22761/GD.2024.0060176GeoAI Dataset for Training a Deep Learning-based GEMS Snow Detection ModelJin-Woo Yu0Jun-Hyeok Jung1Kyoung-Hee Kang2Yong-Mi Lee3Hyung-Sup Jung4 Integrated Master and PhD Student, Department of Geoinformatics, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, 02504 Seoul, South Korea Master Student, Department of Geoinformatics, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, 02504 Seoul, South Korea Senior Researcher, Environmental Satellite Center, National Institute of Environmental Research, 42 Hwangyeong-ro, Seo-gu, 22689 Incheon, South Korea Researcher, Environmental Satellite Center, National Institute of Environmental Research, 42 Hwangyeong-ro, Seo-gu, 22689 Incheon, South Korea Professor, Department of Geoinformatics, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, 02504 Seoul, South KoreaThe Geostationary Environment Monitoring Spectrometer (GEMS) observes air quality across East Asia from an altitude of approximately 36,000 km, analyzing the spatiotemporal distribution of atmospheric pollutants that spread beyond localized regions. GEMS currently provides 21 core air quality-related products, most of which are derived from Level 1C data, which has undergone geometric and radiometric correction. For enhanced accuracy in air quality analysis, precise surface reflectance estimation is essential. However, high-reflectance elements, such as snow, interfere with the accurate estimation of radiance values, necessitating precise detection of such areas. Despite this, GEMS relies solely on the ultraviolet and partial visible bands, lacking the infrared bands crucial for snow detection, and it has no proprietary snow detection algorithm, instead utilizing near-real-time ice and snow extent data from the U.S. National Snow and Ice Data Center. Recently, deep learning techniques have shown potential in image processing, outperforming traditional algorithms, which could address these limitations. However, there is currently no deep learning training dataset available for snow detection specifically for GEMS. To address this issue, this study developed a GeoAI dataset for training a deep learning-based snow detection model for GEMS. In this research, we constructed input data using GEMS Level 1C data and generated label data based on GEMS, Advanced Meteorological Imager, and MODIS snow cover data. The snow detection dataset developed in this study is expected to address the snow detection limitations of GEMS, providing foundational data to enhance the reliability of future geostationary satellite-based air quality research.http://geodata.kr/upload/pdf/GD-2024-0060.pdfgeostationary environment monitoring spectrometersnowartificial intelligencesegmentationdataset |
| spellingShingle | Jin-Woo Yu Jun-Hyeok Jung Kyoung-Hee Kang Yong-Mi Lee Hyung-Sup Jung GeoAI Dataset for Training a Deep Learning-based GEMS Snow Detection Model Geo Data geostationary environment monitoring spectrometer snow artificial intelligence segmentation dataset |
| title | GeoAI Dataset for Training a Deep Learning-based GEMS Snow Detection Model |
| title_full | GeoAI Dataset for Training a Deep Learning-based GEMS Snow Detection Model |
| title_fullStr | GeoAI Dataset for Training a Deep Learning-based GEMS Snow Detection Model |
| title_full_unstemmed | GeoAI Dataset for Training a Deep Learning-based GEMS Snow Detection Model |
| title_short | GeoAI Dataset for Training a Deep Learning-based GEMS Snow Detection Model |
| title_sort | geoai dataset for training a deep learning based gems snow detection model |
| topic | geostationary environment monitoring spectrometer snow artificial intelligence segmentation dataset |
| url | http://geodata.kr/upload/pdf/GD-2024-0060.pdf |
| work_keys_str_mv | AT jinwooyu geoaidatasetfortrainingadeeplearningbasedgemssnowdetectionmodel AT junhyeokjung geoaidatasetfortrainingadeeplearningbasedgemssnowdetectionmodel AT kyoungheekang geoaidatasetfortrainingadeeplearningbasedgemssnowdetectionmodel AT yongmilee geoaidatasetfortrainingadeeplearningbasedgemssnowdetectionmodel AT hyungsupjung geoaidatasetfortrainingadeeplearningbasedgemssnowdetectionmodel |