diff --git "a/methaneset-s2-finetune/index.html" "b/methaneset-s2-finetune/index.html" new file mode 100644--- /dev/null +++ "b/methaneset-s2-finetune/index.html" @@ -0,0 +1,4406 @@ + + + + + + + MethaneSET-S2 Finetune: Verified Methane Plume Events from Sentinel-2 for Supervised Learning + + + + + + + + + + + + +
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TACO DATASET DOCUMENTATION
+

MethaneSET-S2 Finetune: Verified Methane Plume Events from Sentinel-2 for Supervised Learning

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+ v1.0.0 + methaneset-s2-finetune +CC-BY-4.0
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+

Description

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methaneset-s2-finetune is the verified plume subset of MethaneSET-S2, designed for supervised fine-tuning of methane detection and segmentation models. This subset contains Sentinel-2 imagery with manually verified methane plumes, binary segmentation masks, and methane enhancement maps (ΔXCH₄ in ppb). Unlike MARS-S2L which provides only six common bands, MethaneSET retrieves all 13 Sentinel-2 L1C bands at 10m GSD (200x200 pixel chips), enabling research with coastal aerosol, water vapour, cirrus, and red edge channels. Each sample includes target and reference image pairs, plume segmentation masks, CH4 enhancement images, Cloud Score+ masks, wind vectors (ERA5-Land onshore, GEOS-FP offshore), solar/viewing geometry, emission rates with uncertainties, elevation (Copernicus DEM GLO-30), and 64-dim AlphaEarth Foundation embeddings.

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Dataset Overview

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+ 21 partitions + 2018 - 2024 temporal coverage +
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Spatial Coverage

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Click on any region to view partition details

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Keywords

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+ methane + finetune + supervised + segmentation + plume-detection + remote-sensing + Sentinel-2 + MSI + earth-observation + deep-learning +
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ML Tasks

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+ segmentation + classification + detection +
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TACO Structure (Root-Sibling Uniform Tree)

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+ Hierarchical structure showing representative samples across levels. + The "..." notation indicates additional samples following the same pattern. + All samples at the same level share identical structure (RSUT constraint). +

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Hierarchy Details

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LevelTypesTotal SamplesSample IDs (preview)
Level 0All FOLDER3,612Root level samples
Level 1FILE + FILE + FILE + FILE + FILE18,060target, reference, ch4...
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Metadata Fields by Level

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+ These fields are available for querying with SQL when using TacoReader. +

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LEVEL0 (40 fields)
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Field NameTypeDescription
idstringUnique sample identifier within parent scope. Must be unique among siblings.
typestringSample type discriminator (FILE or FOLDER).
stac:crsstringCoordinate reference system (WKT2, EPSG, or PROJ)
stac:tensor_shapelist<item: int64>Raster dimensions [bands, height, width]
stac:geotransformlist<item: double>GDAL affine transform
stac:time_starttimestamp[us]Start timestamp (μs since Unix epoch, UTC)
stac:centroidbinaryCenter point in EPSG:4326 (WKB)
stac:time_endtimestamp[us]End timestamp (μs since Unix epoch, UTC)
stac:time_middletimestamp[us]Middle timestamp (μs since Unix epoch, UTC)
detection:isplumeboolWhether a methane plume is present
detection:ch4_fluxratefloatMethane flux rate (kg/h)
detection:ch4_fluxrate_stdfloatStandard deviation of flux rate
detection:sectorstringEmission sector (Oil and Gas, Coal, Waste, etc.)
detection:offshoreboolWhether location is offshore
detection:wind_sourcestringWind data source (e.g. ERA5-Land, GEOS-FP)
detection:case_studystringCase study area name (e.g. Permian Basin)
satellite:platformstringSatellite platform (S2A, S2B, LC08, LC09)
satellite:tilestringProduct identifier
satellite:vzafloatViewing zenith angle (degrees)
satellite:szafloatSolar zenith angle (degrees)
satellite:background_tilestringReference image product identifier
quality:percentage_clearfloatPercentage of clear pixels (0-100)
quality:observabilitystringImage quality classification
quality:notifiedboolWhether observation has been notified
quality:last_updatestringLast registry modification timestamp (ISO format)
plume:geometrybinaryPlume extent as WKB geometry
site:countrystringCountry of the emission source
site:location_namestringSite location identifier
meteo:wind_ufloatU-component of wind at 10m (m/s)
meteo:wind_vfloatV-component of wind at 10m (m/s)
splitstringDataset partition identifier (train, test, or validation)
majortom:codestringMajorTOM spherical grid cell identifier (e.g., 0100km_0003U_0005R) with ~dist_km spacing
geoenrich:elevationfloatMean elevation in meters (GLO-30 DEM)
geoenrich:temperaturefloatMean annual temperature in °C estimated from MODIS LST data
geoenrich:populationfloatPopulation density from HRSL. Facebook High Resolution Settlement Layer
geoenrich:admin_countriesstringCountry name at centroid location
geoenrich:admin_statesstringState/province name at centroid location
geoenrich:admin_districtsstringDistrict/county name at centroid location
internal:current_idint64Current sample position at this level (0-indexed). Enables O(1) random access and relational JOINs (ZIP, FOLDER, TACOCAT).
internal:parent_idint64Foreign key referencing parent sample position in previous level (ZIP, FOLDER, TACOCAT).
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LEVEL1 (7 fields)
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Field NameTypeDescription
idstringUnique sample identifier within parent scope. Must be unique among siblings.
typestringSample type discriminator (FILE or FOLDER).
geotiff:statslist<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:headerbinaryBinary TACOTIFF header (35 bytes + tile counts) for fast reading without IFD parsing
internal:current_idint64Current sample position at this level (0-indexed). Enables O(1) random access and relational JOINs (ZIP, FOLDER, TACOCAT).
internal:parent_idint64Foreign key referencing parent sample position in previous level (ZIP, FOLDER, TACOCAT).
internal:relative_pathstringRelative path from DATA/ directory. Format: {parent_path}/{id} or {id} for level0 (ZIP, FOLDER, TACOCAT).
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Loading the Dataset

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# pip install tacoreader
+import tacoreader
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+# Load dataset
+ds = tacoreader.load("methaneset-s2-finetune.tacozip")
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+# Basic info
+print(f"ID: {ds.id}")
+print(f"Version: {ds.version}")
+print(f"Samples: {len(ds.data)}")
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Providers & Curators

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Data Providers

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+ UNEP IMEO +producer
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+ Source Cooperative +host
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Dataset Curators

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NameOrganizationEmail
Cesar AybarUniversitat de València, Image and Signal Processing (ISP) Groupcesar.aybar@uv.es
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Publications & Citations

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How to Cite This Dataset

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If you use this dataset in your research, please cite:

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

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@dataset{methaneset-s2-finetune1,
+  title = {MethaneSET-S2 Finetune: Verified Methane Plume Events from Sentinel-2 for 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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