Published 2024 | Version v1
Dataset Open

Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part I.

  • 1. Centro Nacional de Supercómputo, Instituto Potosino de Investigación Científica y Tecnológica A.C., Camino a la Presa de San José No. 2055. Colonia Lomas 4ta Sección, San Luis Potosí, San Luis Potosí. C.P. 78216, México.
  • 2. División de Geociencias Aplicadas, Instituto Potosino de Investigación Científica y Tecnológica A.C., Camino a la Presa de San José No. 2055. Colonia Lomas 4ta Sección, San Luis Potosí, San Luis Potosí. C.P. 78216, México.

Description

This image dataset: "Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part I", is the first part of the image dataset for train and validate deep learning models for oil spill detection and segmentation.

This part contains only the training and validation images for Oil spill.

The dataset comprises Sentinel-1 SAR images in Sigma0, in decibels (db), along with their ground truth. The images are 2048x2048x2, also the ground truth is 2048x2048; all of them are in TIFF format.

The files are organized in the following manner:

  • 01_Train_Val_Oil_Spill_images consist of 1200 Sentinel-1 SAR Sigma0 images, in db, that correspond to oil spills.
  • 01_Train_Val_Oil_Spill_mask: There are 1200 images in the database corresponding to the ground truth of Sentinel-1 SAR Sigma0 images of oil spills. The foreground is assigned a value of 1, while the background is assigned a value of 0.

Each corresponding ground truth has the same number as its respective image. For instance, the image of an oil spill has a corresponding number of 0001, as well as its ground truth.

 

The complete dataset consists of three parts:

Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part I. (10.5281/zenodo.8346860)

Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part II. (10.5281/zenodo.8253899)

Sentinel-1 SAR Oil spill image dataset for train, validate, and test deep learning models. Part III. (10.5281/zenodo.13761290)

Notes

Note that only the Sentinel-1 Sigma0 images in decibels (db) with two polarizations (VV, VH) and dimensions of 2048x2048x2 are georeferenced. The masks or ground truth of each of these images are not georeferenced because they were treated as matrices for the purpose of training, validation, and testing of deep learning models.

Files

Files (40.7 GB)

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md5:e2a6a5b473ca587474d8daee9cd54e10
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md5:9bc53c38db2ab82d15bf6914352403ef
6.2 MB Download

Additional details

Related works

Documents
Journal article: 10.1016/j.marpolbul.2024.116549 (DOI)