isp-uv-es/bradd-s1ts-taco

BraDD-S1TS

This is a repackaging, not a new dataset. It is BraDD-S1TS by Institute for Computational Science, University of Zurich (Karaman 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-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{karaman2023bradd,
  title   = {Deforestation detection in the Amazon with Sentinel-1 SAR image time series},
  author  = {Karaman, Kaan and Sainte Fare Garnot, Vivien and Wegner, Jan Dirk},
  journal = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
  volume  = {X-1/W1-2023},
  pages   = {835--842},
  year    = {2023},
  doi     = {10.5194/isprs-annals-X-1-W1-2023-835-2023}
}

About the data

Deforestation detection in the Brazilian Amazon from Sentinel-1 GRD time series: 25988 series of 48x48 pixels at 10-65 acquisitions (VV/VH, sigma-0 in dB), each with a binary deforestation mask for the observed window and a second mask of the clearing that predates it.

25,988 samples · splits: test 5,251 · train 15,625 · validation 5,112 · tasks: change-detection

Packaged as TACO v3.

Full description

SAR only. The deforestation season is the wet season, which optical sensors spend under cloud.

Sampling. The release's seven sampling_type strategies are carried verbatim: one positive and six distinct negatives (boundary, oldDeforest, forest, herbaceous, agriculture, shrubs), which are not interchangeable.

Geometry. No CRS, transform or coordinates are published for these patches, so none are claimed; the Brazilian state is the only location given.

Splits. Official close_set: 15625/5112/5251.

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/bradd-s1ts-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("bradd-s1ts")
sample = ds[0]                      # {slot name: SlotValue}, arrays decoded
sample["sar"].array.shape

Metadata without decoding anything:

import taco
taco.read("bradd-s1ts/.tacocat")              # one Arrow table, levels joined

Samples

What a sample contains

role	slot	holds	modality	detail
input	sar	raster_series	sar	2 band(s), unit dB, physical
input	deforested_before	mask	label_raster	2 classes
target	deforestation	mask	label_raster	2 classes

Licence

CC-BY-4.0

Providers: Institute for Computational Science, University of Zurich (Karaman 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.