Research on Building AI Learning Dataset for Synthetic Aperture Radar Waterbody Detection through Optical Satellite Image Fusion
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 requir...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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GeoAI Data Society
2023-09-01
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| Series: | Geo Data |
| Subjects: | |
| Online Access: | http://geodata.kr/upload/pdf/GD-2023-0029.pdf |
| _version_ | 1797659535528165376 |
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| author | Joonhyuk Choi Ki-mook Kang Euiho Hwang |
| author_facet | Joonhyuk Choi Ki-mook Kang Euiho Hwang |
| author_sort | Joonhyuk Choi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T18:15:30Z |
| format | Article |
| id | doaj.art-c01197e2885e4073a2e35f1f2c1bbd94 |
| institution | Directory Open Access Journal |
| issn | 2713-5004 |
| language | English |
| last_indexed | 2024-03-11T18:15:30Z |
| publishDate | 2023-09-01 |
| publisher | GeoAI Data Society |
| record_format | Article |
| series | Geo Data |
| spelling | doaj.art-c01197e2885e4073a2e35f1f2c1bbd942023-10-16T07:52:32ZengGeoAI Data SocietyGeo Data2713-50042023-09-015317718410.22761/GD.2023.002998Research on Building AI Learning Dataset for Synthetic Aperture Radar Waterbody Detection through Optical Satellite Image FusionJoonhyuk Choi0Ki-mook Kang1Euiho Hwang2Researcher, Water Resources & Environmental Research Center, K-water Research Institute, 125 Yuseongdae-ro 1689beon-gil, Yuseong-gu, 34045 Daejeon, South KoreaSenior Researcher, Water Resources & Environmental Research Center, K-water Research Institute, 125 Yuseongdae-ro 1689beon-gil, Yuseong-gu, 34045 Daejeon, South KoreaHead of Center, Water Resources & Environmental Research Center, K-water Research Institute, 125 Yuseongdae-ro 1689beon-gil, Yuseong-gu, 34045 Daejeon, South KoreaFor 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.http://geodata.kr/upload/pdf/GD-2023-0029.pdfai datasetssentinel-1sentinel-2fusionwaterbody |
| spellingShingle | Joonhyuk Choi Ki-mook Kang Euiho Hwang Research on Building AI Learning Dataset for Synthetic Aperture Radar Waterbody Detection through Optical Satellite Image Fusion Geo Data ai datasets sentinel-1 sentinel-2 fusion waterbody |
| title | Research on Building AI Learning Dataset for Synthetic Aperture Radar Waterbody Detection through Optical Satellite Image Fusion |
| title_full | Research on Building AI Learning Dataset for Synthetic Aperture Radar Waterbody Detection through Optical Satellite Image Fusion |
| title_fullStr | Research on Building AI Learning Dataset for Synthetic Aperture Radar Waterbody Detection through Optical Satellite Image Fusion |
| title_full_unstemmed | Research on Building AI Learning Dataset for Synthetic Aperture Radar Waterbody Detection through Optical Satellite Image Fusion |
| title_short | Research on Building AI Learning Dataset for Synthetic Aperture Radar Waterbody Detection through Optical Satellite Image Fusion |
| title_sort | research on building ai learning dataset for synthetic aperture radar waterbody detection through optical satellite image fusion |
| topic | ai datasets sentinel-1 sentinel-2 fusion waterbody |
| url | http://geodata.kr/upload/pdf/GD-2023-0029.pdf |
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