MethaneSET: Unified Multi-Sensor Datasets for Satellite-Based Methane Plume Detection

Authors: Cesar Aybar, Julio Contreras, David Montero, Miguel D. Mahecha, Luis Gómez-Chova

Paper: Scientific Data (under review)

Methane is the second-largest driver of anthropogenic warming, and a disproportionate share of emissions comes from a small number of super-emitters detectable by satellite. MethaneSET provides analysis-ready datasets for methane plume detection spanning three sensors with expert-verified segmentation masks from two independent monitoring systems (IMEO MARS and Carbon Mapper). For multispectral sensors (Sentinel-2, Landsat-8/9), labeled scenes with plume masks and confirmed plume-free references are provided for change-detection workflows. For the EMIT imaging spectrometer, calibrated radiance cubes and precomputed matched-filter products support both end-to-end and retrieval-based detection. A synthetic plume bank of 238,545 projected WRF-LES enhancements, plus the 1,647 raw simulation cubes it is built from, enables physics-based data augmentation. All datasets follow the TACO specification and are distributed as Parquet catalogs with Cloud-Optimized GeoTIFFs.

Dataset	Samples	Size	Datacard
methaneset-s2-pretraining	56,344	36.91 GB	view
methaneset-s2-finetune	3,552	6.4 GB	view
methaneset-l89-pretraining	21,919	8.88 GB	view
methaneset-l89-finetune	1,353	1.2 GB	view
methaneset-emit	721	1,005 GB	view
methaneset-bank	238,545	5.1 GB	view
methaneset-bank-les	1,647	3.1 GB	view

What each dataset contains

• methaneset-s2-pretraining / methaneset-l89-pretraining: plume-free scenes for self-supervised pretraining: 13-band (Sentinel-2) or 6-band (Landsat-8/9) chips at 10 m, 200$\times$200 px, each with a plume-free reference, Copernicus DEM elevation and observation metadata.
• methaneset-s2-finetune / methaneset-l89-finetune: plume scenes with expert annotations and four clean backgrounds: target, bg0 (the MARS reference), bg1--bg3 (curated here), the MBMP ch4 enhancement, the binary plume mask and the DEM.
• methaneset-emit: 721 EMIT granules (700 with plumes + 21 confirmed plume-free): the 285-band L1B radiance cube (COG with Zstandard and SNR-adaptive bit discarding), dual plume masks from IMEO and Carbon Mapper, three matched-filter retrievals (mf, rmf, mag1c), an ERA5-Land 10 m wind field, Copernicus DEM elevation, and the per-pixel lat/lon and GLT geometry layers.
• methaneset-bank / methaneset-bank-les: 238,545 projected methane enhancements from WRF-LES simulations across a grid of wind speeds, solar geometries and source configurations, together with the 1,647 source 3D tracer cubes so enhancements can be regenerated for any observation geometry.

Usage

# pip install tacoreader
import tacoreader

# load any dataset straight from the Hub
ds = tacoreader.load(
    "https://huggingface.co/datasets/tacofoundation/methaneset/resolve/main/methaneset-s2-finetune/.tacocat/"
)
print(ds.id, len(ds.data))

# methaneset-emit ships as a folder TACO
ds = tacoreader.load(
    "https://huggingface.co/datasets/tacofoundation/methaneset/resolve/main/methaneset-emit/"
)

Each datacard (linked above) documents the full column list of its dataset.

Citation

@article{aybar2026methaneset,
  title   = {MethaneSET: Unified Multi-Sensor Datasets for Satellite-Based Methane Plume Detection},
  author  = {Aybar, Cesar and Contreras, Julio and Montero, David and Mahecha, Miguel D. and G{\'o}mez-Chova, Luis},
  journal = {Scientific Data},
  year    = {2026}
}