Development of Deep Intelligence for Automatic River Detection (RivDet)
Recently, the impact of climate change has led to an increase in the scale and frequency of extreme rainfall and flash floods. Due to this, the occurrence of floods and various river disasters has increased, necessitating the acquisition of technologies to prevent river disasters. Owing to the natur...
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MDPI AG
2025-01-01
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| author | Sejeong Lee Yejin Kong Taesam Lee |
| author_facet | Sejeong Lee Yejin Kong Taesam Lee |
| author_sort | Sejeong Lee |
| collection | DOAJ |
| description | Recently, the impact of climate change has led to an increase in the scale and frequency of extreme rainfall and flash floods. Due to this, the occurrence of floods and various river disasters has increased, necessitating the acquisition of technologies to prevent river disasters. Owing to the nature of rivers, areas with poor accessibility exist, and obtaining information over a wide area can be time-consuming. Artificial intelligence technology, which has the potential to overcome these limits, has not been broadly adopted for river detection. Therefore, the current study conducted a performance analysis of artificial intelligence for automatic river path setting via the YOLOv8 model, which is widely applied in various fields. Through the augmentation feature in the Roboflow platform, many river images were employed to train and analyze the river spatial information of each applied image. The overall results revealed that the models with augmentation performed better than the basic models without augmentation. In particular, the flip and crop and shear model showed the highest performance with a score of 0.058. When applied to rivers, the Wosucheon stream showed the highest average confidence across all models, with a value of 0.842. Additionally, the max confidence for each river was extracted, and it was found that models including crop exhibited higher reliability. The results show that the augmentation models better generalize new data and can improve performance in real-world environments. Additionally, the RivDet artificial intelligence model for automatic river path configuration developed in the current study is expected to solve various problems, such as automatic flow rate estimation for river disaster prevention, setting early flood warnings, and calculating the range of flood inundation damage. |
| format | Article |
| id | doaj-art-3514bcb4a4c246408888a726c0741887 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-3514bcb4a4c246408888a726c07418872025-01-24T13:48:12ZengMDPI AGRemote Sensing2072-42922025-01-0117234610.3390/rs17020346Development of Deep Intelligence for Automatic River Detection (RivDet)Sejeong Lee0Yejin Kong1Taesam Lee2Department of Civil Engineering, Gyeongsang National University, 501 Jinju-daero, Jinju 52828, Republic of KoreaDepartment of Civil Engineering, Gyeongsang National University, 501 Jinju-daero, Jinju 52828, Republic of KoreaDepartment of Civil Engineering, Gyeongsang National University, 501 Jinju-daero, Jinju 52828, Republic of KoreaRecently, the impact of climate change has led to an increase in the scale and frequency of extreme rainfall and flash floods. Due to this, the occurrence of floods and various river disasters has increased, necessitating the acquisition of technologies to prevent river disasters. Owing to the nature of rivers, areas with poor accessibility exist, and obtaining information over a wide area can be time-consuming. Artificial intelligence technology, which has the potential to overcome these limits, has not been broadly adopted for river detection. Therefore, the current study conducted a performance analysis of artificial intelligence for automatic river path setting via the YOLOv8 model, which is widely applied in various fields. Through the augmentation feature in the Roboflow platform, many river images were employed to train and analyze the river spatial information of each applied image. The overall results revealed that the models with augmentation performed better than the basic models without augmentation. In particular, the flip and crop and shear model showed the highest performance with a score of 0.058. When applied to rivers, the Wosucheon stream showed the highest average confidence across all models, with a value of 0.842. Additionally, the max confidence for each river was extracted, and it was found that models including crop exhibited higher reliability. The results show that the augmentation models better generalize new data and can improve performance in real-world environments. Additionally, the RivDet artificial intelligence model for automatic river path configuration developed in the current study is expected to solve various problems, such as automatic flow rate estimation for river disaster prevention, setting early flood warnings, and calculating the range of flood inundation damage.https://www.mdpi.com/2072-4292/17/2/346riverartificial intelligenceRoboflowYOLOv8augmentation |
| spellingShingle | Sejeong Lee Yejin Kong Taesam Lee Development of Deep Intelligence for Automatic River Detection (RivDet) Remote Sensing river artificial intelligence Roboflow YOLOv8 augmentation |
| title | Development of Deep Intelligence for Automatic River Detection (RivDet) |
| title_full | Development of Deep Intelligence for Automatic River Detection (RivDet) |
| title_fullStr | Development of Deep Intelligence for Automatic River Detection (RivDet) |
| title_full_unstemmed | Development of Deep Intelligence for Automatic River Detection (RivDet) |
| title_short | Development of Deep Intelligence for Automatic River Detection (RivDet) |
| title_sort | development of deep intelligence for automatic river detection rivdet |
| topic | river artificial intelligence Roboflow YOLOv8 augmentation |
| url | https://www.mdpi.com/2072-4292/17/2/346 |
| work_keys_str_mv | AT sejeonglee developmentofdeepintelligenceforautomaticriverdetectionrivdet AT yejinkong developmentofdeepintelligenceforautomaticriverdetectionrivdet AT taesamlee developmentofdeepintelligenceforautomaticriverdetectionrivdet |