Datasets:
MethaneSET-L89 Pretraining: Plume-Free Landsat 8/9 Scenes for Self-Supervised Learning
methaneset-l89-pretraining is the plume-free subset of MethaneSET-L89, designed for self-supervised pretraining of methane detection models. This subset contains Landsat 8/9 imagery from locations and time periods where no methane plumes were detected, providing clean background scenes for learning spectral representations of oil/gas infrastructure, geological features, and atmospheric conditions without methane signatures. Unlike MARS-S2L which provides only six common bands, MethaneSET retrieves all 9 Landsat OLI bands at 10m GSD (200x200 pixel chips), enabling research with coastal aerosol, cirrus, and panchromatic channels. Each sample includes target and reference image pairs, Cloud Score+ masks, wind vectors (ERA5-Land onshore, GEOS-FP offshore), solar/viewing geometry, elevation (Copernicus DEM GLO-30), and 64-dim AlphaEarth Foundation embeddings.
Dataset Information
Version: 1.0.0
License: CC-BY-4.0
Keywords: methane, pretraining, self-supervised, remote-sensing, Landsat-8, Landsat-9, OLI, foundation-model, representation-learning, earth-observation, deep-learning
Tasks: regression, classification, segmentation
Dataset Overview
Partitions: 37 files Spatial coverage: [-121.91, -50.75, 151.42, 51.35] (WGS84) Temporal coverage: 2018-01-05 to 2024-12-31
Dataset Structure (Root-Sibling Uniform Tree)
Root: FOLDER (21,926 samples)
Hierarchy:
- Level 1: FILE → FILE → FILE (65,778 samples)
Metadata Fields
LEVEL0
| Field | Type | Description |
|---|---|---|
id |
string |
Unique sample identifier within parent scope. Must be unique among siblings. |
type |
string |
Sample type discriminator (FILE or FOLDER). |
stac:crs |
string |
Coordinate reference system (WKT2, EPSG, or PROJ) |
stac:tensor_shape |
list<item: int64> |
Raster dimensions [bands, height, width] |
stac:geotransform |
list<item: double> |
GDAL affine transform |
stac:time_start |
timestamp[us] |
Start timestamp (μs since Unix epoch, UTC) |
stac:centroid |
binary |
Center point in EPSG:4326 (WKB) |
stac:time_end |
timestamp[us] |
End timestamp (μs since Unix epoch, UTC) |
stac:time_middle |
timestamp[us] |
Middle timestamp (μs since Unix epoch, UTC) |
detection:isplume |
bool |
Whether a methane plume is present |
detection:ch4_fluxrate |
float |
Methane flux rate (kg/h) |
detection:ch4_fluxrate_std |
float |
Standard deviation of flux rate |
detection:sector |
string |
Emission sector (Oil and Gas, Coal, Waste, etc.) |
detection:offshore |
bool |
Whether location is offshore |
detection:wind_source |
string |
Wind data source (e.g. ERA5-Land, GEOS-FP) |
detection:case_study |
string |
Case study area name (e.g. Permian Basin) |
satellite:platform |
string |
Satellite platform (S2A, S2B, LC08, LC09) |
satellite:tile |
string |
Product identifier |
satellite:vza |
float |
Viewing zenith angle (degrees) |
satellite:sza |
float |
Solar zenith angle (degrees) |
satellite:background_tile |
string |
Reference image product identifier |
quality:percentage_clear |
float |
Percentage of clear pixels (0-100) |
quality:observability |
string |
Image quality classification |
quality:notified |
bool |
Whether observation has been notified |
quality:last_update |
string |
Last registry modification timestamp (ISO format) |
site:country |
string |
Country of the emission source |
site:location_name |
string |
Site location identifier |
meteo:wind_u |
float |
U-component of wind at 10m (m/s) |
meteo:wind_v |
float |
V-component of wind at 10m (m/s) |
split |
string |
Dataset partition identifier (train, test, or validation) |
majortom:code |
string |
MajorTOM spherical grid cell identifier (e.g., 0100km_0003U_0005R) with ~dist_km spacing |
geoenrich:elevation |
float |
Mean elevation in meters (GLO-30 DEM) |
geoenrich:temperature |
float |
Mean annual temperature in °C estimated from MODIS LST data |
geoenrich:population |
float |
Population density from HRSL. Facebook High Resolution Settlement Layer |
geoenrich:admin_countries |
string |
Country name at centroid location |
geoenrich:admin_states |
string |
State/province name at centroid location |
geoenrich:admin_districts |
string |
District/county name at centroid location |
internal:current_id |
int64 |
Current sample position at this level (0-indexed). Enables O(1) random access and relational JOINs (ZIP, FOLDER, TACOCAT). |
internal:parent_id |
int64 |
Foreign key referencing parent sample position in previous level (ZIP, FOLDER, TACOCAT). |
LEVEL1
| Field | Type | Description |
|---|---|---|
id |
string |
Unique sample identifier within parent scope. Must be unique among siblings. |
type |
string |
Sample type discriminator (FILE or FOLDER). |
geotiff:stats |
list<item: list<item: float>> |
Per-band statistics (List[List[Float32]]): categorical mode returns class probabilities, continuous mode returns [min, max, mean, std, valid%, p25, p50, p75, p95] |
taco:header |
binary |
Binary TACOTIFF header (35 bytes + tile counts) for fast reading without IFD parsing |
internal:current_id |
int64 |
Current sample position at this level (0-indexed). Enables O(1) random access and relational JOINs (ZIP, FOLDER, TACOCAT). |
internal:parent_id |
int64 |
Foreign key referencing parent sample position in previous level (ZIP, FOLDER, TACOCAT). |
internal:relative_path |
string |
Relative path from DATA/ directory. Format: {parent_path}/{id} or {id} for level0 (ZIP, FOLDER, TACOCAT). |
Usage
Python
# pip install tacoreader
import tacoreader
ds = tacoreader.load("methaneset-l89-pretraining.tacozip")
print(f"ID: {ds.id}")
print(f"Version: {ds.version}")
print(f"Samples: {len(ds.data)}")
R
# Coming soon: R support is planned but not yet available
# install.packages("tacoreader")
library(tacoreader)
ds <- load_taco("methaneset-l89-pretraining.tacozip")
cat(sprintf("ID: %s\n", ds$id))
cat(sprintf("Version: %s\n", ds$version))
cat(sprintf("Samples: %d\n", nrow(ds$data)))
Julia
# Coming soon: Julia support is planned but not yet available
# using Pkg; Pkg.add("TacoReader")
using TacoReader
ds = load_taco("methaneset-l89-pretraining.tacozip")
println("ID: ", ds.id)
println("Version: ", ds.version)
println("Samples: ", size(ds.data, 1))
Data Providers
UNEP IMEO — producer
Source Cooperative — host
Dataset Curators
| Name | Organization | |
|---|---|---|
| Cesar Aybar | Universitat de València, Image and Signal Processing (ISP) Group | cesar.aybar@uv.es |
Publications & Citations
If you use this dataset in your research, please cite:
DOI: 10.48550/arXiv.2411.15452
Vaughan, A., Mateo-Garcia, G., Irakulis-Loitxate, I., Watine, M., Fernandez-Poblaciones, P., Turner, R. E., Requeima, J., Gorroño, J., Randles, C., Caltagirone, M., & Cifarelli, C.* (2024). AI for operational methane emitter monitoring from space. arXiv preprint arXiv:2411.15452.
Operational MARS-S2L system for global methane monitoring from Sentinel-2 and Landsat 8/9.
DOI: 10.48550/arXiv.2511.21777
Vaughan, A., Mateo-Garcia, G., Irakulis-Loitxate, I., Watine, M., Fernandez-Poblaciones, P., Turner, R. E., Requeima, J., Gorroño, J., Randles, C., Caltagirone, M., & Cifarelli, C.* (2024). Artificial intelligence for methane detection: from continuous monitoring to verified mitigation. arXiv preprint arXiv:2511.21777.
Extended operational deployment demonstrating 1,015 stakeholder notifications across 20 countries and verified permanent mitigation of six persistent emitters.
DOI: 10.5194/essd-13-4349-2021
Muñoz-Sabater, J., et al. (2021). ERA5-Land: a state-of-the-art global reanalysis. Earth System Science Data, 13, 4349-4383.
DOI: 10.1029/2014JD022685
Lucchesi, R. (2013). GEOS-5 FP (Forward Processing) File Specification. NASA GMAO Technical Report.
BibTeX
@dataset{methaneset-l89-pretraining1,
title = {MethaneSET-L89 Pretraining: Plume-Free Landsat 8/9 Scenes for Self-Supervised Learning},
author = {Cesar Aybar},
year = {2018},
version = {1.0.0},
publisher = {Universitat de València, Image and Signal Processing (ISP) Group}
}
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