Monitoring water reservoirs extent with Segment Anything Model applied to Sentinel imagery
Water reservoirs are an essential resource for human health, natural ecosystems, and socio-economic activities, making their effective monitoring mandatory for informed decision-making on sustainable water management. Particularly important is monitoring the extent and level, which enable determinat...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | European Journal of Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2025.2527248 |
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| author | G. Sergi F. Bocchino R. Ravanelli M. Crespi |
| author_facet | G. Sergi F. Bocchino R. Ravanelli M. Crespi |
| author_sort | G. Sergi |
| collection | DOAJ |
| description | Water reservoirs are an essential resource for human health, natural ecosystems, and socio-economic activities, making their effective monitoring mandatory for informed decision-making on sustainable water management. Particularly important is monitoring the extent and level, which enable determination of volume variations. In this respect, this work investigates the performance of Segment Anything Model (SAM) – a foundation model for segmentation released by Meta AI Research – in segmenting water bodies from medium-resolution satellite imagery. SAM was applied in its original form to Sentinel-1 and Sentinel-2 images through prompt engineering (seed modality), testing five different 3-band combinations of the input images in two areas of the Ligurian coast (Italy). Overall, the study demonstrates the adaptability and efficiency of SAM in segmenting water bodies. Among the tested configurations, the SAR 3-band combination achieved the best performance, with [Formula: see text] scores ranging from 0.874 to 0.994. Furthermore, SAM performs better in simpler scenarios with uniform radiometric properties and regular water boundaries, achieving [Formula: see text] score close to 1 with seeds in any position within the water body. Conversely, in more complex scenarios, accurate seed prompt placement becomes critical; analysis of [Formula: see text] maps revealed that seeds placed near the edge of the water, where sharp gradients occur, significantly improve SAM segmentation performance. |
| format | Article |
| id | doaj-art-32b232338fda451ea8500df868e8f708 |
| institution | OA Journals |
| issn | 2279-7254 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj-art-32b232338fda451ea8500df868e8f7082025-08-20T02:36:00ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542025-12-0158110.1080/22797254.2025.2527248Monitoring water reservoirs extent with Segment Anything Model applied to Sentinel imageryG. Sergi0F. Bocchino1R. Ravanelli2M. Crespi3Department of Civil, Building and Environmental Engineering (DICEA), Geodesy and Geomatics Division - Sapienza University of Rome, Rome, ItalyDepartment of Civil, Building and Environmental Engineering (DICEA), Geodesy and Geomatics Division - Sapienza University of Rome, Rome, ItalyGeomatics Unit, Department of Geography, University of Liège, Liège, BelgiumDepartment of Civil, Building and Environmental Engineering (DICEA), Geodesy and Geomatics Division - Sapienza University of Rome, Rome, ItalyWater reservoirs are an essential resource for human health, natural ecosystems, and socio-economic activities, making their effective monitoring mandatory for informed decision-making on sustainable water management. Particularly important is monitoring the extent and level, which enable determination of volume variations. In this respect, this work investigates the performance of Segment Anything Model (SAM) – a foundation model for segmentation released by Meta AI Research – in segmenting water bodies from medium-resolution satellite imagery. SAM was applied in its original form to Sentinel-1 and Sentinel-2 images through prompt engineering (seed modality), testing five different 3-band combinations of the input images in two areas of the Ligurian coast (Italy). Overall, the study demonstrates the adaptability and efficiency of SAM in segmenting water bodies. Among the tested configurations, the SAR 3-band combination achieved the best performance, with [Formula: see text] scores ranging from 0.874 to 0.994. Furthermore, SAM performs better in simpler scenarios with uniform radiometric properties and regular water boundaries, achieving [Formula: see text] score close to 1 with seeds in any position within the water body. Conversely, in more complex scenarios, accurate seed prompt placement becomes critical; analysis of [Formula: see text] maps revealed that seeds placed near the edge of the water, where sharp gradients occur, significantly improve SAM segmentation performance.https://www.tandfonline.com/doi/10.1080/22797254.2025.2527248Water reservoir extent monitoringCopernicus sentinelsfoundation modelssegment anything model |
| spellingShingle | G. Sergi F. Bocchino R. Ravanelli M. Crespi Monitoring water reservoirs extent with Segment Anything Model applied to Sentinel imagery European Journal of Remote Sensing Water reservoir extent monitoring Copernicus sentinels foundation models segment anything model |
| title | Monitoring water reservoirs extent with Segment Anything Model applied to Sentinel imagery |
| title_full | Monitoring water reservoirs extent with Segment Anything Model applied to Sentinel imagery |
| title_fullStr | Monitoring water reservoirs extent with Segment Anything Model applied to Sentinel imagery |
| title_full_unstemmed | Monitoring water reservoirs extent with Segment Anything Model applied to Sentinel imagery |
| title_short | Monitoring water reservoirs extent with Segment Anything Model applied to Sentinel imagery |
| title_sort | monitoring water reservoirs extent with segment anything model applied to sentinel imagery |
| topic | Water reservoir extent monitoring Copernicus sentinels foundation models segment anything model |
| url | https://www.tandfonline.com/doi/10.1080/22797254.2025.2527248 |
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