Multisensor Data Fusion for Coastal Boundary Detection by Res-U-Net Implementation Using High-Resolution UAV Imagery
Environmental planning, hazard monitoring, and coastal management depend critically on accurate shoreline definition. This work utilizes high-resolution UAV data to develop a deep learning framework based on a Residual U-Net architecture for shoreline semantic segmentation. The proposed model integr...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11061779/ |
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| author | Qin Wang Nyasha J. Kavhiza Fakhrul Islam Ilyas Ahmad Huqqani Mohsin Abbas Sanjoy Barman |
| author_facet | Qin Wang Nyasha J. Kavhiza Fakhrul Islam Ilyas Ahmad Huqqani Mohsin Abbas Sanjoy Barman |
| author_sort | Qin Wang |
| collection | DOAJ |
| description | Environmental planning, hazard monitoring, and coastal management depend critically on accurate shoreline definition. This work utilizes high-resolution UAV data to develop a deep learning framework based on a Residual U-Net architecture for shoreline semantic segmentation. The proposed model integrates residual learning blocks into the conventional U-Net architecture to enhance gradient flow, improve feature extraction, and preserve fine boundary details in challenging coastal settings. Under a supervised learning framework, the model has been trained and validated using a dataset including UAV-acquired photographs and manually annotated shoreline masks. The preprocessed input data has been reinforced by geometric adjustments and contrast normalizing to improve resilience and generalization. The Adam optimizer and binary cross-entropy loss helped the model be trained across 150 epochs. F1-score and intersection over union (IoU) measures have been used in quantitative performance evaluation. With a peak validation F1-score of 0.9483 and an IoU of 0.9018, the findings demonstrate that the Residual U-Net achieves great segmentation accuracy, showing robust spatial alignment with ground truth annotations. Visual analysis of the expected masks confirmed the approach’s applicability to real-world situations by revealing consistent coastline localization throughout diverse environmental circumstances. This work presents a scalable and accurate method for operational shoreline monitoring, demonstrating the potential of deep residual structures for coastal boundary mapping using UAV platforms. Large-scale geospatial analytics and real-time coastal change detection can both benefit from the framework’s extension to multitemporal and multisensor data. |
| format | Article |
| id | doaj-art-a422c1a15c81482d83c836b5a1b3b8fa |
| institution | Matheson Library |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-a422c1a15c81482d83c836b5a1b3b8fa2025-07-17T23:00:33ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118167221673210.1109/JSTARS.2025.358485311061779Multisensor Data Fusion for Coastal Boundary Detection by Res-U-Net Implementation Using High-Resolution UAV ImageryQin Wang0https://orcid.org/0009-0004-3954-7879Nyasha J. Kavhiza1https://orcid.org/0000-0002-8505-3253Fakhrul Islam2Ilyas Ahmad Huqqani3Mohsin Abbas4https://orcid.org/0009-0001-5913-5415Sanjoy Barman5https://orcid.org/0009-0005-5387-5059School of Civil, Aerospace and Design Engineering, University of Bristol, Bristol, U.K.Department of Environmental Management, Institute of Environmental Engineering, RUDN University, Moscow, RussiaState Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, ChinaGeoinformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, Minden, MalaysiaDepartment of Hydraulic Engineering, Tsinghua University, Beijing, ChinaDepartment of Geography and Applied Geography, University of North Bengal, Siliguri, IndiaEnvironmental planning, hazard monitoring, and coastal management depend critically on accurate shoreline definition. This work utilizes high-resolution UAV data to develop a deep learning framework based on a Residual U-Net architecture for shoreline semantic segmentation. The proposed model integrates residual learning blocks into the conventional U-Net architecture to enhance gradient flow, improve feature extraction, and preserve fine boundary details in challenging coastal settings. Under a supervised learning framework, the model has been trained and validated using a dataset including UAV-acquired photographs and manually annotated shoreline masks. The preprocessed input data has been reinforced by geometric adjustments and contrast normalizing to improve resilience and generalization. The Adam optimizer and binary cross-entropy loss helped the model be trained across 150 epochs. F1-score and intersection over union (IoU) measures have been used in quantitative performance evaluation. With a peak validation F1-score of 0.9483 and an IoU of 0.9018, the findings demonstrate that the Residual U-Net achieves great segmentation accuracy, showing robust spatial alignment with ground truth annotations. Visual analysis of the expected masks confirmed the approach’s applicability to real-world situations by revealing consistent coastline localization throughout diverse environmental circumstances. This work presents a scalable and accurate method for operational shoreline monitoring, demonstrating the potential of deep residual structures for coastal boundary mapping using UAV platforms. Large-scale geospatial analytics and real-time coastal change detection can both benefit from the framework’s extension to multitemporal and multisensor data.https://ieeexplore.ieee.org/document/11061779/Beachline detectioncoastal monitoringdeep learningmultisensor data fusionremote sensingresidual U-Net |
| spellingShingle | Qin Wang Nyasha J. Kavhiza Fakhrul Islam Ilyas Ahmad Huqqani Mohsin Abbas Sanjoy Barman Multisensor Data Fusion for Coastal Boundary Detection by Res-U-Net Implementation Using High-Resolution UAV Imagery IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Beachline detection coastal monitoring deep learning multisensor data fusion remote sensing residual U-Net |
| title | Multisensor Data Fusion for Coastal Boundary Detection by Res-U-Net Implementation Using High-Resolution UAV Imagery |
| title_full | Multisensor Data Fusion for Coastal Boundary Detection by Res-U-Net Implementation Using High-Resolution UAV Imagery |
| title_fullStr | Multisensor Data Fusion for Coastal Boundary Detection by Res-U-Net Implementation Using High-Resolution UAV Imagery |
| title_full_unstemmed | Multisensor Data Fusion for Coastal Boundary Detection by Res-U-Net Implementation Using High-Resolution UAV Imagery |
| title_short | Multisensor Data Fusion for Coastal Boundary Detection by Res-U-Net Implementation Using High-Resolution UAV Imagery |
| title_sort | multisensor data fusion for coastal boundary detection by res u net implementation using high resolution uav imagery |
| topic | Beachline detection coastal monitoring deep learning multisensor data fusion remote sensing residual U-Net |
| url | https://ieeexplore.ieee.org/document/11061779/ |
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