# 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

```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

```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

```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 | Email |
|------|--------------|-------|
| 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., &amp; 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., &amp; 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.

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**DOI**: 10.1029/2014JD022685

Lucchesi, R. (2013). GEOS-5 FP (Forward Processing) File Specification. NASA GMAO Technical Report.

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### BibTeX

```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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