isp-uv-es/seefar-spot-taco

SeeFar V0 - SPOT 6/7

This is a repackaging, not a new dataset. It is SeeFar V0 - SPOT 6/7 by Airbus Defence and Space / WorldStrat, Coastal Carbon (SeeFar assembly; Lowman et al.), 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-NC-4.0 (Airbus SPOT 6/7 via WorldStrat)

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{lowman2024seefar,
  title   = {SeeFar: Satellite Agnostic Multi-Resolution Dataset for Geospatial Foundation Models},
  author  = {Lowman, James and Zheng, Kelly Liu and Fraser, Roydon and The, Jesse Van Griensven and Valipour, Mojtaba},
  journal = {arXiv preprint arXiv:2406.06776},
  year    = {2024}
}

About the data

The spot half of SeeFar V0: 33611 four-band (blue, green, red, NIR) tiles from SPOT 6/7 at 1.5 m (the WorldStrat half). No labels -- this is a multi-resolution self-supervised pretraining corpus.

33,611 samples · splits: test 5,042 · train 23,527 · validation 5,042 · tasks: similarity-search

Packaged as TACO v3.

Full description

Splits. The release's own: 23527 train, 5042 test, 5042 val. The release spells the last one validate; the partition is untouched and split_original keeps its word.

Scope. SeeFar ships four sources in one archive and they are not co-registered: the four id spaces are independent, the four-way filename intersection is 337 of 23,527 in train and 1 in test, and for 120 of those 337 the closest same-name tile in another source is 546 km away, with 0 of 360 pairs within 10 km. Each source is therefore its own collection and nothing may be joined across them; shared_stem records the filename coincidence.

Geometry. Tiles are in per-tile UTM zones, so resolution_m is measured geodesically per sample. The band table is the release's own per-tile wavelength tags, asserted on every file.

Radiometry. digital_number: no conversion to a physical unit is published for this half, so none is declared and the TACO reader returns no physical values. A scale of x 1e-4 is not supported -- reduced that way the 40 most-vegetated tiles average 0.023 blue, 0.026 green, 0.017 red and 0.127 NIR, and a NIR of 0.127 is far too dark for the canopy those tiles clearly hold. A wrong scale is the one declaration nothing downstream can catch, so the numbers stay uncalibrated and the measurement is recorded here.

nodata. The files declare none; 0 is nonetheless padding, because over 250 tiles 0.748% of pixels are zero in every band against 0.752% in any, so a zero is essentially never a single-band reading.

Getting started

git clone --recursive https://github.com/OscarPellicer/taco
pip install -e "taco/python[ml]"     # builds the reader: C++23, CMake, Ninja, pkg-config, libcurl >= 7.83, OpenSSL >= 3

Read it straight from the Hub:

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

path = snapshot_download("isp-uv-es/seefar-spot-taco", repo_type="dataset")
ds = Dataset(path)                  # the repository is the container: parts + .tacocat
plot_sample(ds[0])

or from a local copy:

ds = Dataset("seefar-spot")
sample = ds[0]                      # {slot name: SlotValue}, arrays decoded
sample["image"].array.shape

Metadata without decoding anything:

import taco
taco.read("seefar-spot/.tacocat")              # one Arrow table, levels joined

Samples

What a sample contains

role	slot	holds	modality	detail
input	image	raster	optical	4 band(s), digital_number

Licence

CC-BY-NC-4.0 (Airbus SPOT 6/7 via WorldStrat)

Terms of use:

• Non-commercial use only (CC-BY-NC-4.0).

Required credits:

• SPOT 6/7 imagery © Airbus DS, distributed with authorisation from Airbus
• WorldStrat: Cornebise, Orsolic and Kalaitzis
• SeeFar: Coastal Carbon (Lowman et al.)

Providers: Airbus Defence and Space / WorldStrat, Coastal Carbon (SeeFar assembly; Lowman et al.)

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.