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| Main Authors: | , , , , , , |
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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.17366635 |
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| _version_ | 1866902272190971904 |
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| author | Dandabathula, Giribabu Roy, Subham Ghatage, Omkar Shashikant Salunkhe, Sagar Subhashrao Nandani, Durgesh Bera, Apurba Kumar Srivastav, Sushil Kumar |
| author_facet | Dandabathula, Giribabu Roy, Subham Ghatage, Omkar Shashikant Salunkhe, Sagar Subhashrao Nandani, Durgesh Bera, Apurba Kumar Srivastav, Sushil Kumar |
| contents | <p>This dataset provides the georeferenced delineation of sandy beach extents along the Indian coastline, derived from IRS ResourceSat-2/2A LISS-IV multispectral imagery using a U-Net deep learning model. The dataset captures fine-scale sandy shoreline features and serves as a foundational resource for coastal geomorphology, shoreline monitoring, and sustainable coastal zone management.</p> <p>The sandy beach polygons were extracted through a deep learning workflow implemented in PyTorch, employing a U-Net segmentation model trained on manually annotated coastal sites representing diverse geomorphic and sedimentary settings. The input imagery comprises 5.8 m spatial resolution LISS-IV data with green, red, and near-infrared bands, supplemented by derived indices such as NDVI, Green–NIR ratio, and composite intensity to improve feature separability.</p> <p>The shapefile provides polygon representations of mapped sandy beaches across the Indian coastline and is projected in WGS 84 geographic coordinates (EPSG:4326). Users can integrate this dataset within GIS environments for visualization, spatial analysis, and model validation.</p> <p>Temporal coverage of the imagery used for mapping spans 2021–2024. Users should note that very small or seasonally transient sandy patches may not be captured due to the 5.8 m spatial resolution of the source data. </p> <p>This dataset supports applications in coastal mapping, blue economy planning, sediment dynamics research, and shoreline change assessment.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17366635 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A National-scale Sandy Beach Dataset for India Derived from High-resolution Satellite Imagery and Deep Learning Dandabathula, Giribabu Roy, Subham Ghatage, Omkar Shashikant Salunkhe, Sagar Subhashrao Nandani, Durgesh Bera, Apurba Kumar Srivastav, Sushil Kumar Sandy Beaches Deep Learning U-Net LISS-IV India <p>This dataset provides the georeferenced delineation of sandy beach extents along the Indian coastline, derived from IRS ResourceSat-2/2A LISS-IV multispectral imagery using a U-Net deep learning model. The dataset captures fine-scale sandy shoreline features and serves as a foundational resource for coastal geomorphology, shoreline monitoring, and sustainable coastal zone management.</p> <p>The sandy beach polygons were extracted through a deep learning workflow implemented in PyTorch, employing a U-Net segmentation model trained on manually annotated coastal sites representing diverse geomorphic and sedimentary settings. The input imagery comprises 5.8 m spatial resolution LISS-IV data with green, red, and near-infrared bands, supplemented by derived indices such as NDVI, Green–NIR ratio, and composite intensity to improve feature separability.</p> <p>The shapefile provides polygon representations of mapped sandy beaches across the Indian coastline and is projected in WGS 84 geographic coordinates (EPSG:4326). Users can integrate this dataset within GIS environments for visualization, spatial analysis, and model validation.</p> <p>Temporal coverage of the imagery used for mapping spans 2021–2024. Users should note that very small or seasonally transient sandy patches may not be captured due to the 5.8 m spatial resolution of the source data. </p> <p>This dataset supports applications in coastal mapping, blue economy planning, sediment dynamics research, and shoreline change assessment.</p> |
| title | A National-scale Sandy Beach Dataset for India Derived from High-resolution Satellite Imagery and Deep Learning |
| topic | Sandy Beaches Deep Learning U-Net LISS-IV India |
| url | https://doi.org/10.5281/zenodo.17366635 |