isp-uv-es/starcop-taco

STARCOP methane plume detection

This is a repackaging, not a new dataset. It is STARCOP methane plume detection by NASA JPL / AVIRIS-NG, Trillium Technologies / SpaceML (Růžička 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

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{ruzicka2023semantic,
  title   = {Semantic segmentation of methane plumes with hyperspectral machine learning models},
  author  = {R{\r{u}}{\v{z}}i{\v{c}}ka, V{\'i}t and Mateo-Garcia, Gonzalo and G{\'o}mez-Chova, Luis and Vaughan, Anna and Guanter, Luis and Markham, Andrew},
  journal = {Scientific Reports},
  volume  = {13},
  pages   = {19999},
  year    = {2023},
  doi     = {10.1038/s41598-023-44918-6}
}

About the data

Methane plume detection in airborne imaging spectroscopy: 3767 AVIRIS-NG tiles at 7.2 m over US emitters, 1878 of them containing a plume.

3,767 samples · splits: test 342 · train 3,080 · validation 345 · tasks: semantic-segmentation

Packaged as TACO v3.

Full description

Contents. Each tile carries:

• Eight AVIRIS bands: three visible, five in the 2.0-2.4 um methane absorption region.
• Eight simulated WorldView-3 SWIR bands over the same footprint.
• The mag1c methane enhancement in ppm x m.
• A binary plume mask.

Splits. The release's own, as are the easy / hard / random difficulty labels.

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:

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/starcop-taco", "starcop.zip", repo_type="dataset")
ds = Dataset(path)
plot_sample(ds[0])

or from a local copy:

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

Metadata without decoding anything:

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

Samples

What a sample contains

role	slot	holds	modality	detail
input	aviris	raster	multispectral	8 band(s), digital_number
input	wv3	raster	multispectral	8 band(s), digital_number
input	mag1c	raster	atmospheric	1 band(s), unit ppm m, physical
target	plume	mask	label_raster	2 classes

Licence

CC-BY-NC-4.0

Terms of use:

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

Required credits:

• STARCOP: Ruzicka et al. (Trillium Technologies)
• AVIRIS-NG: NASA JPL

Providers: NASA JPL / AVIRIS-NG, Trillium Technologies / SpaceML (Růžička 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.