isp-uv-es/mados-taco

MADOS - Marine Debris and Oil Spill

This is a repackaging, not a new dataset. It is MADOS - Marine Debris and Oil Spill by National Technical University of Athens (Kikaki et al.), ESA Copernicus / Sentinel-2, converted to TACO. Pixel values and labels are kept as released except where the description below says otherwise. All credit belongs to the original authors: if you use it, please cite them and follow their licence.

original dataset · paper · licence: CC-BY-4.0

Repackaged into TACO by the Image and Signal Processing Group (ISP), Universitat de València, within the ELLIOT project.

Citation

Please cite the original work:

@article{kikaki2024mados,
  title   = {Detecting Marine Pollutants and Sea Surface Features with Deep Learning in {Sentinel-2} Imagery},
  author  = {Kikaki, Katerina and Kakogeorgiou, Ioannis and Hoteit, Ibrahim and Karantzalos, Konstantinos},
  journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
  volume  = {210},
  pages   = {39--54},
  year    = {2024},
  doi     = {10.1016/j.isprsjprs.2024.02.017}
}

About the data

2803 Sentinel-2 crops over 174 scenes, annotated with 15 sea surface classes covering marine litter, oil spills and the natural features they are confused with.

2,803 samples · splits: test 728 · train 1,433 · validation 642 · tasks: semantic-segmentation

Packaged as TACO v3.

Full description

Bands. Eleven Rayleigh-corrected reflectance bands, kept at their three native resolutions as separate leaves: 4 at 10 m, 6 at 20 m, 1 at 60 m.

Labels. Sparse polygons. Class 0 means unannotated and is the ignore index.

Splits. The release's own lists: 1433 train, 642 val, 728 test.

Georeferencing. The source rasters carry an identity transform under an EPSG:4326 tag, so they are stored without georeferencing rather than with a false one.

Getting started

git clone https://github.com/OscarPellicer/taco
pip install -e "taco/python[ml]"     # builds the native reader: needs CMake and Ninja

Read it straight from the Hub:

import os
from huggingface_hub import hf_hub_download, snapshot_download
from taco.ml import Dataset, plot_sample

path = hf_hub_download("isp-uv-es/mados-taco", "mados.zip", repo_type="dataset")
ds = Dataset(path)
plot_sample(ds[0])

or from a local copy:

ds = Dataset("mados.zip")
sample = ds[0]                      # {slot name: SlotValue}, arrays decoded
sample["s2_10m"].array.shape

Metadata without decoding anything:

import taco
taco.read("mados.zip")              # one Arrow table, levels joined

Samples

What a sample contains

role	slot	holds	modality	detail
input	s2_10m	raster	optical	4 band(s), unit 1, physical
input	s2_20m	raster	multispectral	6 band(s), unit 1, physical
input	s2_60m	raster	optical	1 band(s), unit 1, physical
input	confidence	mask	label_raster	4 classes
input	report	mask	label_raster	4 classes
target	label	mask	label_raster	16 classes

Licence

CC-BY-4.0

Providers: National Technical University of Athens (Kikaki et al.), ESA Copernicus / Sentinel-2

Acknowledgements

TACO was designed by César Aybar and is specified at https://asterisk.coop/taco/spec/.

Built by Oscar Pellicer within the ELLIOT project at the Image and Signal Processing Group (ISP), Universitat de València.