Data

CSIRO Sentinel-1 SAR image dataset of oil- and non-oil features for machine learning ( Deep Learning )

Commonwealth Scientific and Industrial Research Organisation
Blondeau-Patissier, David ; Schroeder, Thomas ; Diakogiannis, Foivos ; Li, Zhibin
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/4v55-dn16&rft.title=CSIRO Sentinel-1 SAR image dataset of oil- and non-oil features for machine learning ( Deep Learning )&rft.identifier=https://doi.org/10.25919/4v55-dn16&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=What this collection is:\nA curated, binary-classified image dataset of grayscale (1 band) 400 x 400-pixel size, or image chips, in a JPEG format extracted from processed Sentinel-1 Synthetic Aperture Radar (SAR) satellite scenes acquired over various regions of the world, and featuring clear open ocean chips, look-alikes (wind or biogenic features) and oil slick chips.\n\nThis binary dataset contains chips labelled as:\n- 0 for chips not containing any oil features (look-alikes or clean seas) \n- 1 for those containing oil features. \n\nThis binary dataset is imbalanced, and biased towards 0 labelled chips (i.e., no oil features), which correspond to 66% of the dataset.\nChips containing oil features, labelled 1, correspond to 34% of the dataset.\n\nWhy:\nThis dataset can be used for training, validation and/or testing of machine learning, including deep learning, algorithms for the detection of oil features in SAR imagery. Directly applicable for algorithm development for the European Space Agency Sentinel-1 SAR mission (https://sentinel.esa.int/web/sentinel/missions/sentinel-1 ), it may be suitable for the development of detection algorithms for other SAR satellite sensors.\n\nOverview of this dataset:\nTotal number of chips (both classes) is N=5,630\nClass \t 0\t 1\nTotal\t\t3,725\t1,905\n\nFurther information and description is found in the ReadMe file provided (ReadMe_Sentinel1_SAR_OilNoOil_20221215.txt)&rft.creator=Blondeau-Patissier, David &rft.creator=Schroeder, Thomas &rft.creator=Diakogiannis, Foivos &rft.creator=Li, Zhibin &rft.date=2022&rft.edition=v1&rft.relation=https://publications.csiro.au/rpr/pub?pid=csiro:EP197797&rft.relation=https://ecos.csiro.au/satellite-imagery-to-detect-oil-spills/&rft.relation=https://ecos.csiro.au/eye-sky-reef-pollution/&rft.coverage=westlimit=101.40910000000001; southlimit=-26.0723; eastlimit=154.76790000000003; northlimit=21.1192; projection=WGS84&rft_rights=Creative Commons Attribution-ShareAlike 4.0 International Licence https://creativecommons.org/licenses/by-sa/4.0/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO 2022.&rft_subject=Sentinel-1&rft_subject=synthetic aperture radar&rft_subject=SAR&rft_subject=artificial intelligence&rft_subject=AI&rft_subject=machine learning&rft_subject=ML&rft_subject=deep learning&rft_subject=DL&rft_subject=great barrier reef&rft_subject=GBR&rft_subject=singapore&rft_subject=oil slick&rft_subject=oil spill&rft_subject=oil discharge&rft_subject=Environmental management&rft_subject=Environmental management&rft_subject=ENVIRONMENTAL SCIENCES&rft_subject=Software engineering not elsewhere classified&rft_subject=Software engineering&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft.type=dataset&rft.language=English Access the data
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Licence & Rights:

Open Licence view details
CC-BY-SA

Creative Commons Attribution-ShareAlike 4.0 International Licence
https://creativecommons.org/licenses/by-sa/4.0/

Data is accessible online and may be reused in accordance with licence conditions

All Rights (including copyright) CSIRO 2022.

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Brief description

What this collection is:
A curated, binary-classified image dataset of grayscale (1 band) 400 x 400-pixel size, or image chips, in a JPEG format extracted from processed Sentinel-1 Synthetic Aperture Radar (SAR) satellite scenes acquired over various regions of the world, and featuring clear open ocean chips, look-alikes (wind or biogenic features) and oil slick chips.

This binary dataset contains chips labelled as:
- "0" for chips not containing any oil features (look-alikes or clean seas)
- "1" for those containing oil features.

This binary dataset is imbalanced, and biased towards "0" labelled chips (i.e., no oil features), which correspond to 66% of the dataset.
Chips containing oil features, labelled "1", correspond to 34% of the dataset.

Why:
This dataset can be used for training, validation and/or testing of machine learning, including deep learning, algorithms for the detection of oil features in SAR imagery. Directly applicable for algorithm development for the European Space Agency Sentinel-1 SAR mission (https://sentinel.esa.int/web/sentinel/missions/sentinel-1 ), it may be suitable for the development of detection algorithms for other SAR satellite sensors.

Overview of this dataset:
Total number of chips (both classes) is N=5,630
Class \t 0\t 1
Total\t\t3,725\t1,905

Further information and description is found in the ReadMe file provided (ReadMe_Sentinel1_SAR_OilNoOil_20221215.txt)

Available: 2022-12-15

Data time period: 2015-05-01 to 2022-08-31

154.7679,21.1192 154.7679,-26.0723 101.4091,-26.0723 101.4091,21.1192 154.7679,21.1192

128.0885,-2.47655

Subjects
AI | DL | Environmental Sciences | Environmental Management | Environmental Management | GBR | Information and Computing Sciences | ML | SAR | Sentinel-1 | Software Engineering | Software Engineering Not Elsewhere Classified | artificial intelligence | deep learning | great barrier reef | machine learning | oil discharge | oil slick | oil spill | singapore | synthetic aperture radar |

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