Hierarchical pyramid refined U-Net for creating the first 2 m resolution multi-class national-scale spatial distribution dataset of offshore observable marine aquaculture in China
Precise extraction and dynamic monitoring of observable offshore marine aquaculture (OOMA) areas in remote sensing imagery are essential for the rational layout of offshore aquaculture zones and assessing the marine ecological environment. At present, national-scale offshore marine aquaculture spati...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2520471 |
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| author | Yanlin Chen Guojin He Ranyu Yin Guizhou Wang Xueli Peng |
| author_facet | Yanlin Chen Guojin He Ranyu Yin Guizhou Wang Xueli Peng |
| author_sort | Yanlin Chen |
| collection | DOAJ |
| description | Precise extraction and dynamic monitoring of observable offshore marine aquaculture (OOMA) areas in remote sensing imagery are essential for the rational layout of offshore aquaculture zones and assessing the marine ecological environment. At present, national-scale offshore marine aquaculture spatial distribution datasets derived from remote sensing imagery in China are of medium to low resolution, and there remains a lack of high-spatial-resolution (HSR) dataset. To address this gap, this study proposes a novel semantic segmentation framework, Hierarchical Pyramid Refined U-Net (HPR-UNet), based on multi-source 2 m resolution remote sensing imagery for the multi-scale extraction of OOMA. The first HSR OOMA spatial distribution dataset of China (RCdata_2022_2 m) has been generated. The study reveals the following: (1) The proposed method is effective in extracting multi-scale OOMA, especially small-scale ones. (2) The RCdata_2022_2 m achieved an overall accuracy of 97.27%. Compared to medium- and low-resolution datasets, RCdata_2022_2 m demonstrated higher accuracy and better extraction results. (3) From 2022 to 2023, the total area of OOMA in China was approximately 253,096.08 ha, with raft aquaculture occupying 232,523.69 ha, and cage aquaculture covering 20,572.39 ha. |
| format | Article |
| id | doaj-art-6a561b487de04339b2f4aff5bfb54ffd |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-6a561b487de04339b2f4aff5bfb54ffd2025-08-25T11:28:36ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2520471Hierarchical pyramid refined U-Net for creating the first 2 m resolution multi-class national-scale spatial distribution dataset of offshore observable marine aquaculture in ChinaYanlin Chen0Guojin He1Ranyu Yin2Guizhou Wang3Xueli Peng4Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaPrecise extraction and dynamic monitoring of observable offshore marine aquaculture (OOMA) areas in remote sensing imagery are essential for the rational layout of offshore aquaculture zones and assessing the marine ecological environment. At present, national-scale offshore marine aquaculture spatial distribution datasets derived from remote sensing imagery in China are of medium to low resolution, and there remains a lack of high-spatial-resolution (HSR) dataset. To address this gap, this study proposes a novel semantic segmentation framework, Hierarchical Pyramid Refined U-Net (HPR-UNet), based on multi-source 2 m resolution remote sensing imagery for the multi-scale extraction of OOMA. The first HSR OOMA spatial distribution dataset of China (RCdata_2022_2 m) has been generated. The study reveals the following: (1) The proposed method is effective in extracting multi-scale OOMA, especially small-scale ones. (2) The RCdata_2022_2 m achieved an overall accuracy of 97.27%. Compared to medium- and low-resolution datasets, RCdata_2022_2 m demonstrated higher accuracy and better extraction results. (3) From 2022 to 2023, the total area of OOMA in China was approximately 253,096.08 ha, with raft aquaculture occupying 232,523.69 ha, and cage aquaculture covering 20,572.39 ha.https://www.tandfonline.com/doi/10.1080/17538947.2025.2520471Multi-source remote sensing imageryhigh spatial resolutionobservable offshore marine aquaculturesemantic segmentationU-Net |
| spellingShingle | Yanlin Chen Guojin He Ranyu Yin Guizhou Wang Xueli Peng Hierarchical pyramid refined U-Net for creating the first 2 m resolution multi-class national-scale spatial distribution dataset of offshore observable marine aquaculture in China International Journal of Digital Earth Multi-source remote sensing imagery high spatial resolution observable offshore marine aquaculture semantic segmentation U-Net |
| title | Hierarchical pyramid refined U-Net for creating the first 2 m resolution multi-class national-scale spatial distribution dataset of offshore observable marine aquaculture in China |
| title_full | Hierarchical pyramid refined U-Net for creating the first 2 m resolution multi-class national-scale spatial distribution dataset of offshore observable marine aquaculture in China |
| title_fullStr | Hierarchical pyramid refined U-Net for creating the first 2 m resolution multi-class national-scale spatial distribution dataset of offshore observable marine aquaculture in China |
| title_full_unstemmed | Hierarchical pyramid refined U-Net for creating the first 2 m resolution multi-class national-scale spatial distribution dataset of offshore observable marine aquaculture in China |
| title_short | Hierarchical pyramid refined U-Net for creating the first 2 m resolution multi-class national-scale spatial distribution dataset of offshore observable marine aquaculture in China |
| title_sort | hierarchical pyramid refined u net for creating the first 2 m resolution multi class national scale spatial distribution dataset of offshore observable marine aquaculture in china |
| topic | Multi-source remote sensing imagery high spatial resolution observable offshore marine aquaculture semantic segmentation U-Net |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2520471 |
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