task_categories:
- image-segmentation
MARIDA
MARIDA is a dataset for sparsely labeled marine debris which consists of 11 MSI bands. This dataset contains a training set of 694 samples along with a validation set of 328 samples and a test set of 350 samples. All image samples are originally 256 x 256 pixels. Wecombine both the original validation set and test set into one single test set (678 samples). Weemploy the same approach as DFC2020’s where we divide 256 x 256 pixels into 9 smaller patches of 96 x 96 pixels. Thus, our final training set contains 5,622 training samples, 624 validation samples and 6,183 test samples. All images are 96 x 96 pixels.
How to Use This Dataset
from datasets import load_dataset
dataset = load_dataset("GFM-Bench/MARIDA")
Also, please see our GFM-Bench repository for more information about how to use the dataset! 🤗
Dataset Metadata
The following metadata provides details about the Sentinel-2 imagery used in the dataset:
- Number of Sentinel-2 Bands: 11
- Sentinel-2 Bands: B01 (Coastal aerosol), B02 (Blue), B03 (Green), B04 (Red), B05 (Vegetation red edge), B06 (Vegetation red edge), B07 (Vegetation red edge), B08 (NIR), B8A (Narrow NIR), B11 (SWIR), B12 (SWIR)
- Image Resolution: 96 x 96 pixels
- Spatial Resolution: 10 meters
- Number of Classes: 11
Dataset Splits
The MARIDA dataset consists following splits:
- train: 5,622 samples
- val: 624 samples
- test: 6,183 samples
Dataset Features:
The MARIDA dataset consists of following features:
- optical: the Sentinel-2 image.
- label: the segmentation labels.
- optical_channel_wv: the central wavelength of each Sentinel-2 bands.
- spatial_resolution: the spatial resolution of images.
Citation
If you use the MARIDA dataset in your work, please cite the original paper:
@article{kikaki2022marida,
title={MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data},
author={Kikaki, Katerina and Kakogeorgiou, Ioannis and Mikeli, Paraskevi and Raitsos, Dionysios E and Karantzalos, Konstantinos},
journal={PloS one},
volume={17},
number={1},
pages={e0262247},
year={2022},
publisher={Public Library of Science San Francisco, CA USA}
}