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| Main Authors: | , |
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| Format: | Dataset Open Access |
| Language: | en |
| Published: |
PANGAEA
2025
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| Subjects: | |
| Online Access: | https://doi.org/10.1594/PANGAEA.980773 |
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| _version_ | 1867171897947455488 |
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| author | Yang, Yi-Jie Singha, Suman |
| author_facet | Yang, Yi-Jie Singha, Suman |
| collection | Datos científicos de ciencias marinas y ambientales |
| contents | Spaceborne synthetic aperture radar (SAR) has been applied to detect oil slicks. Previous studies applied machine learning and deep learning techniques to automate oil slick detection. However, a large amount of data is generally required to train such a model. It highlights the importance and necessity of a publicly available oil slick dataset. This dataset provides annotations of oil slicks located in longitude from 30°E to 36°E and in latitude from 31°N to 34.7°N in the Eastern Mediterranean Sea, observed from Sentinel-1 SAR in 2019. The annotation and inspection of the oil slicks were initially done in the framework of a previous study by the authors (Yang, Y.-J. et al., 2024). On top of that, images with oceanic and other phenomena, which can also manifest similar SAR signatures and are considered look-alikes, are also included in the dataset as a no-oil set. A well-developed oil spill detection system should be able to not only detect oil spills but also avoid the detection of these look-alikes. These look-alikes were collected on a larger extent, ranging between 27.1212703°E and 36.0881997°E in longitudes and 29.2991798°N and 36.3715771°N in latitudes. There are, in total, 1365 image patches with 3225 oil objects in the oil set and 2290 image patches in the no-oil set. |
| format | Dataset Open Access |
| id | pangaea_https___doi_org_10_1594_PANGAEA_980773 |
| institution | PANGAEA |
| language | en |
| publishDate | 2025 |
| publisher | PANGAEA |
| record_format | pangaea |
| spellingShingle | Oil slicks, look-alikes and other remarkable SAR signatures in Sentinel-1 imagery in the Eastern Mediterranean Sea in 2019 Yang, Yi-Jie Singha, Suman Binary Object; DARTIS_2019; DATE/TIME; Eastern Mediterranean Sea; Height; Identification; Image; Image set; LATITUDE; LONGITUDE; look-alikes; oil slick; Position, X; Position, Y; Satellite imagery; SATI; Sentinel-1; Size; synthetic aperture radar; Width Spaceborne synthetic aperture radar (SAR) has been applied to detect oil slicks. Previous studies applied machine learning and deep learning techniques to automate oil slick detection. However, a large amount of data is generally required to train such a model. It highlights the importance and necessity of a publicly available oil slick dataset. This dataset provides annotations of oil slicks located in longitude from 30°E to 36°E and in latitude from 31°N to 34.7°N in the Eastern Mediterranean Sea, observed from Sentinel-1 SAR in 2019. The annotation and inspection of the oil slicks were initially done in the framework of a previous study by the authors (Yang, Y.-J. et al., 2024). On top of that, images with oceanic and other phenomena, which can also manifest similar SAR signatures and are considered look-alikes, are also included in the dataset as a no-oil set. A well-developed oil spill detection system should be able to not only detect oil spills but also avoid the detection of these look-alikes. These look-alikes were collected on a larger extent, ranging between 27.1212703°E and 36.0881997°E in longitudes and 29.2991798°N and 36.3715771°N in latitudes. There are, in total, 1365 image patches with 3225 oil objects in the oil set and 2290 image patches in the no-oil set. |
| title | Oil slicks, look-alikes and other remarkable SAR signatures in Sentinel-1 imagery in the Eastern Mediterranean Sea in 2019 |
| topic | Binary Object; DARTIS_2019; DATE/TIME; Eastern Mediterranean Sea; Height; Identification; Image; Image set; LATITUDE; LONGITUDE; look-alikes; oil slick; Position, X; Position, Y; Satellite imagery; SATI; Sentinel-1; Size; synthetic aperture radar; Width |
| url | https://doi.org/10.1594/PANGAEA.980773 |