Add files using upload-large-folder tool

methaneset-emit/COLLECTION.json

@@ -0,0 +1,101 @@
+{
+  "id": "methaneset-emit",
+  "dataset_version": "1.0.0",
+  "description": "methaneset-emit provides analysis-ready hyperspectral data from the EMIT imaging spectrometer (ISS) for methane plume detection and quantification. Each sample contains the full L1B radiance cube (285 bands, 381\u20132493 nm) in sensor geometry, three pre-computed retrieval products (matched filter, robust matched filter, and mag1c concentration in ppm\u00b7m), dual plume segmentation masks from UNEP-IMEO and CarbonMapper analysts, Copernicus DEM GLO-30 elevation, and observation geometry (SZA, VZA, AMF, azimuth angles). Unlike multispectral MethaneSET subsets that require temporal image pairs, EMIT enables single-acquisition detection via direct spectral absorption fitting.",
+  "licenses": [
+    "CC-BY-4.0"
+  ],
+  "providers": [
+    {
+      "name": "NASA JPL",
+      "roles": [
+        "producer"
+      ],
+      "url": null,
+      "links": null
+    },
+    {
+      "name": "UNEP IMEO",
+      "roles": [
+        "producer"
+      ],
+      "url": null,
+      "links": null
+    },
+    {
+      "name": "CarbonMapper",
+      "roles": [
+        "producer"
+      ],
+      "url": null,
+      "links": null
+    },
+    {
+      "name": "Hugging Face",
+      "roles": [
+        "host"
+      ],
+      "url": null,
+      "links": null
+    }
+  ],
+  "tasks": [
+    "segmentation",
+    "regression"
+  ],
+  "taco_version": "0.5.0",
+  "title": "MethaneSET-EMIT: Hyperspectral Methane Plume Detection from EMIT",
+  "curators": [
+    {
+      "name": "Cesar Aybar",
+      "organization": "Universitat de Val\u00e8ncia, Image and Signal Processing (ISP) Group",
+      "email": "cesar.aybar@uv.es",
+      "role": null
+    }
+  ],
+  "keywords": [
+    "methane",
+    "hyperspectral",
+    "EMIT",
+    "ISS",
+    "imaging-spectroscopy",
+    "matched-filter",
+    "plume-detection",
+    "segmentation",
+    "retrieval",
+    "remote-sensing",
+    "earth-observation",
+    "deep-learning"
+  ],
+  "extent": {
+    "spatial": [
+      -180.0,
+      -90.0,
+      180.0,
+      90.0
+    ],
+    "temporal": null
+  },
+  "publications": [
+    {
+      "doi": "10.5067/EMIT/EMITL1BRAD.001",
+      "citation": "Green, R. O., et al. (2023). EMIT L1B At-Sensor Calibrated Radiance and Geolocation Data 60 m V001. NASA Land Processes DAAC.",
+      "summary": "EMIT L1B calibrated radiance product used as source imagery."
+    },
+    {
+      "doi": "10.5194/amt-17-1333-2024",
+      "citation": "Roger, J., Guanter, L., Gorro\u00f1o, J., & Irakulis-Loitxate, I. (2024). Exploiting the entire near-infrared spectral range to improve the detection of methane plumes with high-resolution imaging spectrometers. Atmospheric Measurement Techniques, 17, 1333\u20131346.",
+      "summary": "Wide/robust matched filter method used for RMF retrieval product."
+    },
+    {
+      "doi": "10.5194/amt-14-2771-2021",
+      "citation": "Foote, M. D., et al. (2020). Fast and accurate retrieval of methane concentration from imaging spectrometer data using sparsity prior. IEEE Transactions on Geoscience and Remote Sensing, 58(9), 6480\u20136492.",
+      "summary": "mag1c retrieval algorithm for calibrated ppm\u00b7m concentration estimates."
+    },
+    {
+      "doi": "10.48550/arXiv.2411.15452",
+      "citation": "Vaughan, A.*, Mateo-Garcia, G.*, et al. (2024). AI for operational methane emitter monitoring from space. arXiv preprint arXiv:2411.15452.",
+      "summary": "MARS operational system providing IMEO plume masks."
+    }
+  ]
+}

methaneset-emit/README.md

@@ -0,0 +1,126 @@
+
+# MethaneSET-EMIT: Hyperspectral Methane Plume Detection from EMIT
+
+methaneset-emit provides analysis-ready hyperspectral data from the EMIT imaging spectrometer (ISS) for methane plume detection and quantification. Each sample contains the full L1B radiance cube (285 bands, 381–2493 nm) in sensor geometry, three pre-computed retrieval products (matched filter, robust matched filter, and mag1c concentration in ppm·m), dual plume segmentation masks from UNEP-IMEO and CarbonMapper analysts, Copernicus DEM GLO-30 elevation, and observation geometry (SZA, VZA, AMF, azimuth angles). Unlike multispectral MethaneSET subsets that require temporal image pairs, EMIT enables single-acquisition detection via direct spectral absorption fitting.
+## Dataset Information
+
+**Version**: 1.0.0
+
+**License**: CC-BY-4.0
+
+**Keywords**: methane, hyperspectral, EMIT, ISS, imaging-spectroscopy, matched-filter, plume-detection, segmentation, retrieval, remote-sensing, earth-observation, deep-learning
+
+**Tasks**: segmentation, regression
+
+
+
+
+## Usage
+
+### Python
+
+```python
+# pip install tacoreader
+import tacoreader
+
+ds = tacoreader.load("methaneset-emit.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-emit.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-emit.tacozip")
+println("ID: ", ds.id)
+println("Version: ", ds.version)
+println("Samples: ", size(ds.data, 1))
+```
+
+## Data Providers
+
+**NASA JPL** — *producer*
+
+**UNEP IMEO** — *producer*
+
+**CarbonMapper** — *producer*
+
+**Hugging Face** — *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**: 10.5067/EMIT/EMITL1BRAD.001
+
+Green, R. O., et al. (2023). EMIT L1B At-Sensor Calibrated Radiance and Geolocation Data 60 m V001. NASA Land Processes DAAC.
+
+*EMIT L1B calibrated radiance product used as source imagery.*
+
+---
+
+**DOI**: 10.5194/amt-17-1333-2024
+
+Roger, J., Guanter, L., Gorroño, J., &amp; Irakulis-Loitxate, I. (2024). Exploiting the entire near-infrared spectral range to improve the detection of methane plumes with high-resolution imaging spectrometers. Atmospheric Measurement Techniques, 17, 1333–1346.
+
+*Wide/robust matched filter method used for RMF retrieval product.*
+
+---
+
+**DOI**: 10.5194/amt-14-2771-2021
+
+Foote, M. D., et al. (2020). Fast and accurate retrieval of methane concentration from imaging spectrometer data using sparsity prior. IEEE Transactions on Geoscience and Remote Sensing, 58(9), 6480–6492.
+
+*mag1c retrieval algorithm for calibrated ppm·m concentration estimates.*
+
+---
+
+**DOI**: 10.48550/arXiv.2411.15452
+
+Vaughan, A.*, Mateo-Garcia, G.*, et al. (2024). AI for operational methane emitter monitoring from space. arXiv preprint arXiv:2411.15452.
+
+*MARS operational system providing IMEO plume masks.*
+
+---
+
+### BibTeX
+
+```bibtex
+@dataset{methaneset-emit1,
+  title = {MethaneSET-EMIT: Hyperspectral Methane Plume Detection from EMIT},
+  author = {Cesar Aybar},
+  year = {2024},
+  version = {1.0.0},
+  publisher = {Universitat de València, Image and Signal Processing (ISP) Group}
+}
+```
+
+---
+
+Generated with ❤️ using [TacoToolbox](https://github.com/tacotoolbox/tacotoolbox) v0.26.9

methaneset-emit/methaneset-emit/COLLECTION.json

@@ -0,0 +1,374 @@
+{
+    "id": "methaneset-emit",
+    "dataset_version": "1.0.0",
+    "description": "methaneset-emit provides analysis-ready hyperspectral data from the EMIT imaging spectrometer (ISS) for methane plume detection and quantification. Each sample contains the full L1B radiance cube (285 bands, 381–2493 nm) in sensor geometry, three pre-computed retrieval products (matched filter, robust matched filter, and mag1c concentration in ppm·m), dual plume segmentation masks from UNEP-IMEO and CarbonMapper analysts, Copernicus DEM GLO-30 elevation, and observation geometry (SZA, VZA, AMF, azimuth angles). Unlike multispectral MethaneSET subsets that require temporal image pairs, EMIT enables single-acquisition detection via direct spectral absorption fitting.",
+    "licenses": [
+        "CC-BY-4.0"
+    ],
+    "providers": [
+        {
+            "name": "NASA JPL",
+            "roles": [
+                "producer"
+            ],
+            "url": null,
+            "links": null
+        },
+        {
+            "name": "UNEP IMEO",
+            "roles": [
+                "producer"
+            ],
+            "url": null,
+            "links": null
+        },
+        {
+            "name": "CarbonMapper",
+            "roles": [
+                "producer"
+            ],
+            "url": null,
+            "links": null
+        },
+        {
+            "name": "Hugging Face",
+            "roles": [
+                "host"
+            ],
+            "url": null,
+            "links": null
+        }
+    ],
+    "tasks": [
+        "segmentation",
+        "regression"
+    ],
+    "taco_version": "0.5.0",
+    "title": "MethaneSET-EMIT: Hyperspectral Methane Plume Detection from EMIT",
+    "curators": [
+        {
+            "name": "Cesar Aybar",
+            "organization": "Universitat de València, Image and Signal Processing (ISP) Group",
+            "email": "cesar.aybar@uv.es",
+            "role": null
+        }
+    ],
+    "keywords": [
+        "methane",
+        "hyperspectral",
+        "EMIT",
+        "ISS",
+        "imaging-spectroscopy",
+        "matched-filter",
+        "plume-detection",
+        "segmentation",
+        "retrieval",
+        "remote-sensing",
+        "earth-observation",
+        "deep-learning"
+    ],
+    "extent": {
+        "spatial": [
+            -180.0,
+            -90.0,
+            180.0,
+            90.0
+        ],
+        "temporal": null
+    },
+    "publications": [
+        {
+            "doi": "10.5067/EMIT/EMITL1BRAD.001",
+            "citation": "Green, R. O., et al. (2023). EMIT L1B At-Sensor Calibrated Radiance and Geolocation Data 60 m V001. NASA Land Processes DAAC.",
+            "summary": "EMIT L1B calibrated radiance product used as source imagery."
+        },
+        {
+            "doi": "10.5194/amt-17-1333-2024",
+            "citation": "Roger, J., Guanter, L., Gorroño, J., & Irakulis-Loitxate, I. (2024). Exploiting the entire near-infrared spectral range to improve the detection of methane plumes with high-resolution imaging spectrometers. Atmospheric Measurement Techniques, 17, 1333–1346.",
+            "summary": "Wide/robust matched filter method used for RMF retrieval product."
+        },
+        {
+            "doi": "10.5194/amt-14-2771-2021",
+            "citation": "Foote, M. D., et al. (2020). Fast and accurate retrieval of methane concentration from imaging spectrometer data using sparsity prior. IEEE Transactions on Geoscience and Remote Sensing, 58(9), 6480–6492.",
+            "summary": "mag1c retrieval algorithm for calibrated ppm·m concentration estimates."
+        },
+        {
+            "doi": "10.48550/arXiv.2411.15452",
+            "citation": "Vaughan, A.*, Mateo-Garcia, G.*, et al. (2024). AI for operational methane emitter monitoring from space. arXiv preprint arXiv:2411.15452.",
+            "summary": "MARS operational system providing IMEO plume masks."
+        }
+    ],
+    "taco:pit_schema": {
+        "root": {
+            "n": 503,
+            "type": "FOLDER"
+        },
+        "shape": [
+            503,
+            8
+        ],
+        "hierarchy": {
+            "1": [
+                {
+                    "n": 4024,
+                    "type": [
+                        "FILE",
+                        "FILE",
+                        "FILE",
+                        "FILE",
+                        "FILE",
+                        "FILE",
+                        "FILE",
+                        "FILE"
+                    ],
+                    "id": [
+                        "radiance.tif",
+                        "mf.tif",
+                        "rmf.tif",
+                        "mag1c.tif",
+                        "plume_imeo.tif",
+                        "plume_cm.tif",
+                        "elevation.tif",
+                        "glt.tif"
+                    ]
+                }
+            ]
+        }
+    },
+    "taco:field_schema": {
+        "level0": [
+            [
+                "id",
+                "string",
+                "Unique sample identifier within parent scope. Must be unique among siblings."
+            ],
+            [
+                "type",
+                "string",
+                "Sample type discriminator (FILE or FOLDER)."
+            ],
+            [
+                "detection:n_plumes",
+                "int32",
+                "Number of verified plumes in this granule"
+            ],
+            [
+                "detection:sector",
+                "string",
+                "Emission sector (Oil and Gas, Waste, Coal, etc.)"
+            ],
+            [
+                "detection:lat",
+                "double",
+                "Latitude of emission source (EPSG:4326)"
+            ],
+            [
+                "detection:lon",
+                "double",
+                "Longitude of emission source (EPSG:4326)"
+            ],
+            [
+                "detection:wind_speed",
+                "float",
+                "Mean wind speed at emission time (m/s)"
+            ],
+            [
+                "spatial:bbox_west",
+                "double",
+                "Western longitude of bounding box (EPSG:4326)"
+            ],
+            [
+                "spatial:bbox_south",
+                "double",
+                "Southern latitude of bounding box (EPSG:4326)"
+            ],
+            [
+                "spatial:bbox_east",
+                "double",
+                "Eastern longitude of bounding box (EPSG:4326)"
+            ],
+            [
+                "spatial:bbox_north",
+                "double",
+                "Northern latitude of bounding box (EPSG:4326)"
+            ],
+            [
+                "sensor:shape_rows",
+                "int32",
+                "Number of rows in sensor geometry"
+            ],
+            [
+                "sensor:shape_cols",
+                "int32",
+                "Number of columns in sensor geometry"
+            ],
+            [
+                "sensor:sza_min",
+                "float",
+                "Minimum solar zenith angle (degrees)"
+            ],
+            [
+                "sensor:sza_mean",
+                "float",
+                "Mean solar zenith angle (degrees)"
+            ],
+            [
+                "sensor:sza_max",
+                "float",
+                "Maximum solar zenith angle (degrees)"
+            ],
+            [
+                "sensor:vza_min",
+                "float",
+                "Minimum view zenith angle (degrees)"
+            ],
+            [
+                "sensor:vza_mean",
+                "float",
+                "Mean view zenith angle (degrees)"
+            ],
+            [
+                "sensor:vza_max",
+                "float",
+                "Maximum view zenith angle (degrees)"
+            ],
+            [
+                "sensor:amf_mean",
+                "float",
+                "Mean air mass factor (1/cos(SZA) + 1/cos(VZA))"
+            ],
+            [
+                "sensor:sun_azimuth_mean",
+                "float",
+                "Mean sun azimuth angle (degrees)"
+            ],
+            [
+                "sensor:sensor_azimuth_mean",
+                "float",
+                "Mean sensor azimuth angle (degrees)"
+            ],
+            [
+                "sensor:path_length_mean",
+                "float",
+                "Mean atmospheric path length (km)"
+            ],
+            [
+                "sensor:earth_sun_distance",
+                "float",
+                "Earth-Sun distance (AU)"
+            ],
+            [
+                "emit:flight_line",
+                "string",
+                "EMIT flight line identifier"
+            ],
+            [
+                "emit:time_start",
+                "string",
+                "Acquisition start time (ISO 8601)"
+            ],
+            [
+                "emit:time_end",
+                "string",
+                "Acquisition end time (ISO 8601)"
+            ],
+            [
+                "radiance:min",
+                "float",
+                "Minimum radiance value in L1B cube"
+            ],
+            [
+                "radiance:max",
+                "float",
+                "Maximum radiance value in L1B cube"
+            ],
+            [
+                "radiance:elev_min_m",
+                "float",
+                "Minimum terrain elevation (m)"
+            ],
+            [
+                "radiance:elev_max_m",
+                "float",
+                "Maximum terrain elevation (m)"
+            ],
+            [
+                "annotation:pct_imeo",
+                "float",
+                "Plume pixel percentage (UNEP-IMEO mask)"
+            ],
+            [
+                "annotation:pct_cm",
+                "float",
+                "Plume pixel percentage (CarbonMapper mask)"
+            ],
+            [
+                "annotation:iou",
+                "float",
+                "Intersection over Union between IMEO and CM masks"
+            ],
+            [
+                "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)"
+            ],
+            [
+                "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": [
+            [
+                "id",
+                "string",
+                "Unique sample identifier within parent scope. Must be unique among siblings."
+            ],
+            [
+                "type",
+                "string",
+                "Sample type discriminator (FILE or FOLDER)."
+            ],
+            [
+                "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)."
+            ]
+        ]
+    }
+}

methaneset-l89-finetune/.tacocat/COLLECTION.json

@@ -0,0 +1,689 @@
+{
+    "id": "methaneset-l89-finetune",
+    "dataset_version": "1.0.0",
+    "description": "methaneset-l89-finetune is the verified plume subset of MethaneSET-L89, designed for supervised fine-tuning of methane detection and segmentation models. This subset contains Landsat 8/9 imagery with manually verified methane plumes, binary segmentation masks, and methane enhancement maps (\u0394XCH\u2084 in ppb). 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, 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.",
+    "licenses": [
+        "CC-BY-4.0"
+    ],
+    "providers": [
+        {
+            "name": "UNEP IMEO",
+            "roles": [
+                "producer"
+            ],
+            "url": null,
+            "links": null
+        },
+        {
+            "name": "Source Cooperative",
+            "roles": [
+                "host"
+            ],
+            "url": null,
+            "links": null
+        }
+    ],
+    "tasks": [
+        "segmentation",
+        "classification",
+        "detection"
+    ],
+    "taco_version": "0.5.0",
+    "title": "MethaneSET-L89 Finetune: Verified Methane Plume Events from Landsat 8/9 for Supervised Learning",
+    "curators": [
+        {
+            "name": "Cesar Aybar",
+            "organization": "Universitat de Val\u00e8ncia, Image and Signal Processing (ISP) Group",
+            "email": "cesar.aybar@uv.es",
+            "role": null
+        }
+    ],
+    "keywords": [
+        "methane",
+        "finetune",
+        "supervised",
+        "segmentation",
+        "plume-detection",
+        "remote-sensing",
+        "Landsat-8",
+        "Landsat-9",
+        "OLI",
+        "earth-observation",
+        "deep-learning"
+    ],
+    "extent": {
+        "spatial": [
+            -103.98245581079834,
+            -50.74604622590055,
+            151.10422679597286,
+            45.5634371535571
+        ],
+        "temporal": [
+            "2018-01-10T07:01:13.279000Z",
+            "2024-12-30T10:08:34.423000Z"
+        ]
+    },
+    "publications": [
+        {
+            "doi": "10.48550/arXiv.2411.15452",
+            "citation": "Vaughan, A.*, Mateo-Garcia, G.*, Irakulis-Loitxate, I., Watine, M., Fernandez-Poblaciones, P., Turner, R. E., Requeima, J., Gorro\u00f1o, J., Randles, C., Caltagirone, M., & Cifarelli, C.* (2024). AI for operational methane emitter monitoring from space. arXiv preprint arXiv:2411.15452.",
+            "summary": "Operational MARS-S2L system for global methane monitoring from Sentinel-2 and Landsat 8/9."
+        },
+        {
+            "doi": "10.48550/arXiv.2511.21777",
+            "citation": "Vaughan, A.*, Mateo-Garcia, G.*, Irakulis-Loitxate, I., Watine, M., Fernandez-Poblaciones, P., Turner, R. E., Requeima, J., Gorro\u00f1o, J., Randles, C., Caltagirone, M., & Cifarelli, C.* (2024). Artificial intelligence for methane detection: from continuous monitoring to verified mitigation. arXiv preprint arXiv:2511.21777.",
+            "summary": "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",
+            "citation": "Mu\u00f1oz-Sabater, J., et al. (2021). ERA5-Land: a state-of-the-art global reanalysis. Earth System Science Data, 13, 4349-4383.",
+            "summary": null
+        },
+        {
+            "doi": "10.1029/2014JD022685",
+            "citation": "Lucchesi, R. (2013). GEOS-5 FP (Forward Processing) File Specification. NASA GMAO Technical Report.",
+            "summary": null
+        }
+    ],
+    "taco:pit_schema": {
+        "root": {
+            "n": 1548,
+            "type": "FOLDER"
+        },
+        "shape": [
+            300,
+            5
+        ],
+        "hierarchy": {
+            "1": [
+                {
+                    "n": 7740,
+                    "type": [
+                        "FILE",
+                        "FILE",
+                        "FILE",
+                        "FILE",
+                        "FILE"
+                    ],
+                    "id": [
+                        "target",
+                        "reference",
+                        "ch4",
+                        "plume",
+                        "dem"
+                    ]
+                }
+            ]
+        }
+    },
+    "taco:field_schema": {
+        "level0": [
+            [
+                "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 (\u03bcs since Unix epoch, UTC)"
+            ],
+            [
+                "stac:centroid",
+                "binary",
+                "Center point in EPSG:4326 (WKB)"
+            ],
+            [
+                "stac:time_end",
+                "timestamp[us]",
+                "End timestamp (\u03bcs since Unix epoch, UTC)"
+            ],
+            [
+                "stac:time_middle",
+                "timestamp[us]",
+                "Middle timestamp (\u03bcs 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)"
+            ],
+            [
+                "plume:geometry",
+                "binary",
+                "Plume extent as WKB geometry"
+            ],
+            [
+                "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 \u00b0C 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": [
+            [
+                "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)."
+            ]
+        ]
+    },
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+            "methaneset-l89-finetune_Argentina.tacozip",
+            "methaneset-l89-finetune_Australia.tacozip",
+            "methaneset-l89-finetune_Bahrain.tacozip",
+            "methaneset-l89-finetune_China.tacozip",
+            "methaneset-l89-finetune_Syrian_Arab_Republic.tacozip",
+            "methaneset-l89-finetune_Turkmenistan.tacozip",
+            "methaneset-l89-finetune_Egypt.tacozip",
+            "methaneset-l89-finetune_India.tacozip",
+            "methaneset-l89-finetune_Iran_(Islamic_Republic_of).tacozip",
+            "methaneset-l89-finetune_Iraq.tacozip",
+            "methaneset-l89-finetune_Libya.tacozip",
+            "methaneset-l89-finetune_United_States_of_America.tacozip",
+            "methaneset-l89-finetune_Kazakhstan.tacozip",
+            "methaneset-l89-finetune_Yemen.tacozip",
+            "methaneset-l89-finetune_Kuwait.tacozip",
+            "methaneset-l89-finetune_Oman.tacozip",
+            "methaneset-l89-finetune_Saudi_Arabia.tacozip",
+            "methaneset-l89-finetune_Uzbekistan.tacozip",
+            "methaneset-l89-finetune_Venezuela.tacozip"
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+            {
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+            }
+        ]
+    }
+}

methaneset-l89-finetune/README.md

@@ -0,0 +1,189 @@
+
+# MethaneSET-L89 Finetune: Verified Methane Plume Events from Landsat 8/9 for Supervised Learning
+
+methaneset-l89-finetune is the verified plume subset of MethaneSET-L89, designed for supervised fine-tuning of methane detection and segmentation models. This subset contains Landsat 8/9 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 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, 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.
+## Dataset Information
+
+**Version**: 1.0.0
+
+**License**: CC-BY-4.0
+
+**Keywords**: methane, finetune, supervised, segmentation, plume-detection, remote-sensing, Landsat-8, Landsat-9, OLI, earth-observation, deep-learning
+
+**Tasks**: segmentation, classification, detection
+
+## Dataset Overview
+
+**Partitions**: 20 files
+**Spatial coverage**: [-103.98, -50.75, 151.10, 45.56] (WGS84)
+**Temporal coverage**: 2018-01-10 to 2024-12-30
+
+## Dataset Structure (Root-Sibling Uniform Tree)
+
+**Root**: FOLDER (1,548 samples)
+
+**Hierarchy**:
+
+- Level 1: FILE → FILE → FILE → FILE → FILE (7,740 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) |
+| `plume:geometry` | `binary` | Plume extent as WKB geometry |
+| `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-finetune.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-finetune.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-finetune.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.
+
+---
+
+**DOI**: 10.1029/2014JD022685
+
+Lucchesi, R. (2013). GEOS-5 FP (Forward Processing) File Specification. NASA GMAO Technical Report.
+
+---
+
+### BibTeX
+
+```bibtex
+@dataset{methaneset-l89-finetune1,
+  title = {MethaneSET-L89 Finetune: Verified Methane Plume Events from Landsat 8/9 for Supervised Learning},
+  author = {Cesar Aybar},
+  year = {2018},
+  version = {1.0.0},
+  publisher = {Universitat de València, Image and Signal Processing (ISP) Group}
+}
+```
+
+---
+
+Generated with ❤️ using [TacoToolbox](https://github.com/tacotoolbox/tacotoolbox) v0.26.9

methaneset-l89-pretraining/.tacocat/COLLECTION.json

@@ -0,0 +1,952 @@
+{
+    "id": "methaneset-l89-pretraining",
+    "dataset_version": "1.0.0",
+    "description": "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.",
+    "licenses": [
+        "CC-BY-4.0"
+    ],
+    "providers": [
+        {
+            "name": "UNEP IMEO",
+            "roles": [
+                "producer"
+            ],
+            "url": null,
+            "links": null
+        },
+        {
+            "name": "Source Cooperative",
+            "roles": [
+                "host"
+            ],
+            "url": null,
+            "links": null
+        }
+    ],
+    "tasks": [
+        "regression",
+        "classification",
+        "segmentation"
+    ],
+    "taco_version": "0.5.0",
+    "title": "MethaneSET-L89 Pretraining: Plume-Free Landsat 8/9 Scenes for Self-Supervised Learning",
+    "curators": [
+        {
+            "name": "Cesar Aybar",
+            "organization": "Universitat de Val\u00e8ncia, Image and Signal Processing (ISP) Group",
+            "email": "cesar.aybar@uv.es",
+            "role": null
+        }
+    ],
+    "keywords": [
+        "methane",
+        "pretraining",
+        "self-supervised",
+        "remote-sensing",
+        "Landsat-8",
+        "Landsat-9",
+        "OLI",
+        "foundation-model",
+        "representation-learning",
+        "earth-observation",
+        "deep-learning"
+    ],
+    "extent": {
+        "spatial": [
+            -121.90527689115115,
+            -50.74632213836442,
+            151.41903572121242,
+            51.35452375533172
+        ],
+        "temporal": [
+            "2018-01-05T10:02:52.340000Z",
+            "2024-12-31T17:27:23.051000Z"
+        ]
+    },
+    "publications": [
+        {
+            "doi": "10.48550/arXiv.2411.15452",
+            "citation": "Vaughan, A.*, Mateo-Garcia, G.*, Irakulis-Loitxate, I., Watine, M., Fernandez-Poblaciones, P., Turner, R. E., Requeima, J., Gorro\u00f1o, J., Randles, C., Caltagirone, M., & Cifarelli, C.* (2024). AI for operational methane emitter monitoring from space. arXiv preprint arXiv:2411.15452.",
+            "summary": "Operational MARS-S2L system for global methane monitoring from Sentinel-2 and Landsat 8/9."
+        },
+        {
+            "doi": "10.48550/arXiv.2511.21777",
+            "citation": "Vaughan, A.*, Mateo-Garcia, G.*, Irakulis-Loitxate, I., Watine, M., Fernandez-Poblaciones, P., Turner, R. E., Requeima, J., Gorro\u00f1o, J., Randles, C., Caltagirone, M., & Cifarelli, C.* (2024). Artificial intelligence for methane detection: from continuous monitoring to verified mitigation. arXiv preprint arXiv:2511.21777.",
+            "summary": "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",
+            "citation": "Mu\u00f1oz-Sabater, J., et al. (2021). ERA5-Land: a state-of-the-art global reanalysis. Earth System Science Data, 13, 4349-4383.",
+            "summary": null
+        },
+        {
+            "doi": "10.1029/2014JD022685",
+            "citation": "Lucchesi, R. (2013). GEOS-5 FP (Forward Processing) File Specification. NASA GMAO Technical Report.",
+            "summary": null
+        }
+    ],
+    "taco:pit_schema": {
+        "root": {
+            "n": 21926,
+            "type": "FOLDER"
+        },
+        "shape": [
+            3257,
+            3
+        ],
+        "hierarchy": {
+            "1": [
+                {
+                    "n": 65778,
+                    "type": [
+                        "FILE",
+                        "FILE",
+                        "FILE"
+                    ],
+                    "id": [
+                        "target",
+                        "reference",
+                        "dem"
+                    ]
+                }
+            ]
+        }
+    },
+    "taco:field_schema": {
+        "level0": [
+            [
+                "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 (\u03bcs since Unix epoch, UTC)"
+            ],
+            [
+                "stac:centroid",
+                "binary",
+                "Center point in EPSG:4326 (WKB)"
+            ],
+            [
+                "stac:time_end",
+                "timestamp[us]",
+                "End timestamp (\u03bcs since Unix epoch, UTC)"
+            ],
+            [
+                "stac:time_middle",
+                "timestamp[us]",
+                "Middle timestamp (\u03bcs 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 \u00b0C 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": [
+            [
+                "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)."
+            ]
+        ]
+    },
+    "taco:sources": {
+        "count": 37,
+        "ids": [
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining",
+            "methaneset-l89-pretraining"
+        ],
+        "files": [
+            "methaneset-l89-pretraining_Libya.tacozip",
+            "methaneset-l89-pretraining_Angola.tacozip",
+            "methaneset-l89-pretraining_Algeria.tacozip",
+            "methaneset-l89-pretraining_United_Arab_Emirates.tacozip",
+            "methaneset-l89-pretraining_Argentina.tacozip",
+            "methaneset-l89-pretraining_Australia.tacozip",
+            "methaneset-l89-pretraining_Azerbaijan.tacozip",
+            "methaneset-l89-pretraining_Bahrain.tacozip",
+            "methaneset-l89-pretraining_Canada.tacozip",
+            "methaneset-l89-pretraining_China.tacozip",
+            "methaneset-l89-pretraining_Uzbekistan.tacozip",
+            "methaneset-l89-pretraining_Kazakhstan.tacozip",
+            "methaneset-l89-pretraining_Turkmenistan.tacozip",
+            "methaneset-l89-pretraining_Egypt.tacozip",
+            "methaneset-l89-pretraining_Syrian_Arab_Republic.tacozip",
+            "methaneset-l89-pretraining_United_States_of_America.tacozip",
+            "methaneset-l89-pretraining_Iran_(Islamic_Republic_of).tacozip",
+            "methaneset-l89-pretraining_Oman.tacozip",
+            "methaneset-l89-pretraining_India.tacozip",
+            "methaneset-l89-pretraining_Iraq.tacozip",
+            "methaneset-l89-pretraining_Saudi_Arabia.tacozip",
+            "methaneset-l89-pretraining_Yemen.tacozip",
+            "methaneset-l89-pretraining_Pakistan.tacozip",
+            "methaneset-l89-pretraining_Kuwait.tacozip",
+            "methaneset-l89-pretraining_Venezuela.tacozip",
+            "methaneset-l89-pretraining_Spain.tacozip",
+            "methaneset-l89-pretraining_Jordan.tacozip",
+            "methaneset-l89-pretraining_Mexico.tacozip",
+            "methaneset-l89-pretraining_Mozambique.tacozip",
+            "methaneset-l89-pretraining_Nigeria.tacozip",
+            "methaneset-l89-pretraining_Poland.tacozip",
+            "methaneset-l89-pretraining_Qatar.tacozip",
+            "methaneset-l89-pretraining_Romania.tacozip",
+            "methaneset-l89-pretraining_Russian_Federation.tacozip",
+            "methaneset-l89-pretraining_Tunisia.tacozip",
+            "methaneset-l89-pretraining_Italy.tacozip",
+            "methaneset-l89-pretraining_Turkey.tacozip"
+        ],
+        "extents": [
+            {
+                "file": "methaneset-l89-pretraining_Libya.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
+                    9.847921824042594,
+                    26.051820703038985,
+                    22.794418456466452,
+                    31.8007075199224
+                ],
+                "temporal": [
+                    "2018-04-03T09:13:07.898000Z",
+                    "2024-12-31T09:14:09.266000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Angola.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
+                    11.852762587016276,
+                    -5.637213806020404,
+                    11.852762587016276,
+                    -5.637213806020404
+                ],
+                "temporal": [
+                    "2024-05-29T09:22:37.473000Z",
+                    "2024-06-06T09:22:21.258000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Algeria.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
+                    2.848722090758709,
+                    27.423017349742693,
+                    9.919917312298324,
+                    33.12111983792291
+                ],
+                "temporal": [
+                    "2018-01-05T10:02:52.340000Z",
+                    "2024-12-31T10:03:16.027000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_United_Arab_Emirates.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
+                    52.99538432488015,
+                    22.754020965638485,
+                    55.657741626820766,
+                    25.326198180376256
+                ],
+                "temporal": [
+                    "2022-11-08T06:47:24.888000Z",
+                    "2024-12-31T06:47:05.678000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Argentina.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
+                    -70.71361065606807,
+                    -50.74632213836442,
+                    -67.07351145135375,
+                    -36.36516993179736
+                ],
+                "temporal": [
+                    "2022-09-08T14:13:57.825000Z",
+                    "2024-12-27T14:06:56.486000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Australia.tacozip",
+                "id": "methaneset-l89-pretraining",
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+                    "2024-12-29T02:14:47.578000Z"
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+            },
+            {
+                "file": "methaneset-l89-pretraining_Azerbaijan.tacozip",
+                "id": "methaneset-l89-pretraining",
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+                    "2024-12-25T07:19:27.382000Z"
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+            },
+            {
+                "file": "methaneset-l89-pretraining_Bahrain.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
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+                    25.98248462770002,
+                    50.62597884326003,
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+                    "2022-02-06T07:04:50.449000Z",
+                    "2024-12-28T07:04:44.910000Z"
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+            },
+            {
+                "file": "methaneset-l89-pretraining_Canada.tacozip",
+                "id": "methaneset-l89-pretraining",
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+                    "2024-07-06T18:23:15.976000Z"
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+            },
+            {
+                "file": "methaneset-l89-pretraining_China.tacozip",
+                "id": "methaneset-l89-pretraining",
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+                    "2024-12-31T05:03:25.948000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Uzbekistan.tacozip",
+                "id": "methaneset-l89-pretraining",
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+                    "2024-12-28T06:11:51.240000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Kazakhstan.tacozip",
+                "id": "methaneset-l89-pretraining",
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+                    "2024-12-27T07:05:53.537000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Turkmenistan.tacozip",
+                "id": "methaneset-l89-pretraining",
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+                    36.10053720179613,
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+                    "2024-12-27T07:07:05.202000Z"
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+            {
+                "file": "methaneset-l89-pretraining_Egypt.tacozip",
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+                    "2024-12-30T08:30:05.237000Z"
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+            {
+                "file": "methaneset-l89-pretraining_Syrian_Arab_Republic.tacozip",
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+                    "2024-12-28T07:51:55.938000Z"
+                ]
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+            {
+                "file": "methaneset-l89-pretraining_United_States_of_America.tacozip",
+                "id": "methaneset-l89-pretraining",
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+                    "2024-12-31T17:27:23.051000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Iran_(Islamic_Republic_of).tacozip",
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+            {
+                "file": "methaneset-l89-pretraining_Oman.tacozip",
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+                    "2024-12-24T06:41:22.306000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_India.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
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+                    "2024-12-18T05:38:20.858000Z"
+                ]
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+            {
+                "file": "methaneset-l89-pretraining_Iraq.tacozip",
+                "id": "methaneset-l89-pretraining",
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+                    "2024-12-30T07:39:56.733000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Saudi_Arabia.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
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+                    "2024-12-28T07:05:08.814000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Yemen.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
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+                    46.034509320103396,
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+                ],
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+                    "2024-12-26T07:19:54.687000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Pakistan.tacozip",
+                "id": "methaneset-l89-pretraining",
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+                    "2024-02-15T05:56:47.659000Z",
+                    "2024-12-23T05:56:54.762000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Kuwait.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
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+                    28.914391701323723,
+                    48.04045678254083,
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+                ],
+                "temporal": [
+                    "2022-12-29T07:16:42.802000Z",
+                    "2024-12-26T07:16:19.587000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Venezuela.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
+                    -64.88576368399845,
+                    8.716513220540905,
+                    -63.474418553064716,
+                    10.079305592715533
+                ],
+                "temporal": [
+                    "2024-01-18T14:40:31.178000Z",
+                    "2024-12-28T14:34:38.036000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Spain.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
+                    -0.6167705910056326,
+                    38.959486200378045,
+                    -0.45725943052154294,
+                    39.53316117215265
+                ],
+                "temporal": [
+                    "2024-12-25T10:37:37.318000Z",
+                    "2024-12-25T10:37:37.318000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Jordan.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
+                    35.876953288104026,
+                    31.208752398118012,
+                    39.086960792062385,
+                    32.59855742878077
+                ],
+                "temporal": [
+                    "2022-11-04T07:59:23.134000Z",
+                    "2024-12-27T07:58:47.125000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Mexico.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
+                    -98.87923989295848,
+                    18.801378699314174,
+                    -97.58433222436793,
+                    20.694798771439366
+                ],
+                "temporal": [
+                    "2023-08-21T16:53:15.704000Z",
+                    "2024-12-29T16:53:34.956000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Mozambique.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
+                    34.867365728357555,
+                    -21.31759405134149,
+                    34.867365728357555,
+                    -21.31759405134149
+                ],
+                "temporal": [
+                    "2022-11-17T07:43:06.403000Z",
+                    "2023-11-28T07:42:54.141000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Nigeria.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
+                    6.561141288426349,
+                    5.649490842929508,
+                    6.561231887568189,
+                    5.6495808458230385
+                ],
+                "temporal": [
+                    "2024-01-09T09:51:14.105000Z",
+                    "2024-12-18T09:50:59.821000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Poland.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
+                    18.627205288626964,
+                    49.96821335653393,
+                    18.627205288626964,
+                    49.96821335653393
+                ],
+                "temporal": [
+                    "2023-02-15T09:38:53.626000Z",
+                    "2023-09-20T09:32:31.267000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Qatar.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
+                    50.78146803531009,
+                    25.112956481918598,
+                    50.84229363858148,
+                    25.483231028509067
+                ],
+                "temporal": [
+                    "2024-08-30T07:04:29.326000Z",
+                    "2024-12-28T07:04:44.910000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Romania.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
+                    26.127667096229644,
+                    44.31711435413894,
+                    26.127667096229644,
+                    44.31711435413894
+                ],
+                "temporal": [
+                    "2024-06-07T09:02:29.546000Z",
+                    "2024-09-04T08:56:40.122000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Russian_Federation.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
+                    39.73945619447577,
+                    46.73484897064187,
+                    39.73945619447577,
+                    46.73484897064187
+                ],
+                "temporal": [
+                    "2024-02-24T08:12:59.554000Z",
+                    "2024-04-28T08:12:31.691000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Tunisia.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
+                    10.974555041778721,
+                    32.30409067520942,
+                    10.974555041778721,
+                    32.30409067520942
+                ],
+                "temporal": [
+                    "2024-11-07T09:49:36.453000Z",
+                    "2024-12-01T09:49:50.302000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Italy.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
+                    7.693733245321123,
+                    45.0662086847566,
+                    7.693989279773527,
+                    45.06629577434891
+                ],
+                "temporal": [
+                    "2024-04-08T10:16:52.990000Z",
+                    "2024-07-30T10:10:44.603000Z"
+                ]
+            },
+            {
+                "file": "methaneset-l89-pretraining_Turkey.tacozip",
+                "id": "methaneset-l89-pretraining",
+                "spatial": [
+                    40.24537780864526,
+                    40.245276215866525,
+                    40.24549370293862,
+                    40.24536755386647
+                ],
+                "temporal": [
+                    "2024-05-08T08:01:49.301000Z",
+                    "2024-12-10T08:02:36.945000Z"
+                ]
+            }
+        ]
+    }
+}

methaneset-l89-pretraining/README.md

@@ -0,0 +1,188 @@
+
+# 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.
+
+---
+
+**DOI**: 10.1029/2014JD022685
+
+Lucchesi, R. (2013). GEOS-5 FP (Forward Processing) File Specification. NASA GMAO Technical Report.
+
+---
+
+### 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}
+}
+```
+
+---
+
+Generated with ❤️ using [TacoToolbox](https://github.com/tacotoolbox/tacotoolbox) v0.26.9

methaneset-s2-finetune/.tacocat/COLLECTION.json

@@ -0,0 +1,704 @@
+{
+    "id": "methaneset-s2-finetune",
+    "dataset_version": "1.0.0",
+    "description": "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 (\u0394XCH\u2084 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.",
+    "licenses": [
+        "CC-BY-4.0"
+    ],
+    "providers": [
+        {
+            "name": "UNEP IMEO",
+            "roles": [
+                "producer"
+            ],
+            "url": null,
+            "links": null
+        },
+        {
+            "name": "Source Cooperative",
+            "roles": [
+                "host"
+            ],
+            "url": null,
+            "links": null
+        }
+    ],
+    "tasks": [
+        "segmentation",
+        "classification",
+        "detection"
+    ],
+    "taco_version": "0.5.0",
+    "title": "MethaneSET-S2 Finetune: Verified Methane Plume Events from Sentinel-2 for Supervised Learning",
+    "curators": [
+        {
+            "name": "Cesar Aybar",
+            "organization": "Universitat de Val\u00e8ncia, Image and Signal Processing (ISP) Group",
+            "email": "cesar.aybar@uv.es",
+            "role": null
+        }
+    ],
+    "keywords": [
+        "methane",
+        "finetune",
+        "supervised",
+        "segmentation",
+        "plume-detection",
+        "remote-sensing",
+        "Sentinel-2",
+        "MSI",
+        "earth-observation",
+        "deep-learning"
+    ],
+    "extent": {
+        "spatial": [
+            -103.98229730043207,
+            -36.36548079964959,
+            116.52207519781263,
+            49.96807008152752
+        ],
+        "temporal": [
+            "2018-01-05T07:13:01Z",
+            "2024-12-30T09:13:09Z"
+        ]
+    },
+    "publications": [
+        {
+            "doi": "10.48550/arXiv.2411.15452",
+            "citation": "Vaughan, A.*, Mateo-Garcia, G.*, Irakulis-Loitxate, I., Watine, M., Fernandez-Poblaciones, P., Turner, R. E., Requeima, J., Gorro\u00f1o, J., Randles, C., Caltagirone, M., & Cifarelli, C.* (2024). AI for operational methane emitter monitoring from space. arXiv preprint arXiv:2411.15452.",
+            "summary": "Operational MARS-S2L system for global methane monitoring from Sentinel-2 and Landsat 8/9."
+        },
+        {
+            "doi": "10.48550/arXiv.2511.21777",
+            "citation": "Vaughan, A.*, Mateo-Garcia, G.*, Irakulis-Loitxate, I., Watine, M., Fernandez-Poblaciones, P., Turner, R. E., Requeima, J., Gorro\u00f1o, J., Randles, C., Caltagirone, M., & Cifarelli, C.* (2024). Artificial intelligence for methane detection: from continuous monitoring to verified mitigation. arXiv preprint arXiv:2511.21777.",
+            "summary": "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",
+            "citation": "Mu\u00f1oz-Sabater, J., et al. (2021). ERA5-Land: a state-of-the-art global reanalysis. Earth System Science Data, 13, 4349-4383.",
+            "summary": null
+        },
+        {
+            "doi": "10.1029/2014JD022685",
+            "citation": "Lucchesi, R. (2013). GEOS-5 FP (Forward Processing) File Specification. NASA GMAO Technical Report.",
+            "summary": null
+        }
+    ],
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+            "type": "FOLDER"
+        },
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+                {
+                    "n": 18060,
+                    "type": [
+                        "FILE",
+                        "FILE",
+                        "FILE",
+                        "FILE",
+                        "FILE"
+                    ],
+                    "id": [
+                        "target",
+                        "reference",
+                        "ch4",
+                        "plume",
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+                "id",
+                "string",
+                "Unique sample identifier within parent scope. Must be unique among siblings."
+            ],
+            [
+                "type",
+                "string",
+                "Sample type discriminator (FILE or FOLDER)."
+            ],
+            [
+                "stac:crs",
+                "string",
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+            ],
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+                "list<item: int64>",
+                "Raster dimensions [bands, height, width]"
+            ],
+            [
+                "stac:geotransform",
+                "list<item: double>",
+                "GDAL affine transform"
+            ],
+            [
+                "stac:time_start",
+                "timestamp[us]",
+                "Start timestamp (\u03bcs since Unix epoch, UTC)"
+            ],
+            [
+                "stac:centroid",
+                "binary",
+                "Center point in EPSG:4326 (WKB)"
+            ],
+            [
+                "stac:time_end",
+                "timestamp[us]",
+                "End timestamp (\u03bcs since Unix epoch, UTC)"
+            ],
+            [
+                "stac:time_middle",
+                "timestamp[us]",
+                "Middle timestamp (\u03bcs since Unix epoch, UTC)"
+            ],
+            [
+                "detection:isplume",
+                "bool",
+                "Whether a methane plume is present"
+            ],
+            [
+                "detection:ch4_fluxrate",
+                "float",
+                "Methane flux rate (kg/h)"
+            ],
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+                "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)"
+            ],
+            [
+                "plume:geometry",
+                "binary",
+                "Plume extent as WKB geometry"
+            ],
+            [
+                "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 \u00b0C 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": [
+            [
+                "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)."
+            ]
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+                    "2024-12-05T07:33:01Z"
+                ]
+            },
+            {
+                "file": "methaneset-s2-finetune_Oman.tacozip",
+                "id": "methaneset-s2-finetune",
+                "spatial": [
+                    55.61462943211787,
+                    22.870209853654913,
+                    55.61462943211787,
+                    22.870209853654913
+                ],
+                "temporal": [
+                    "2023-11-24T06:51:59Z",
+                    "2024-01-08T06:52:51Z"
+                ]
+            },
+            {
+                "file": "methaneset-s2-finetune_Poland.tacozip",
+                "id": "methaneset-s2-finetune",
+                "spatial": [
+                    18.62771543924463,
+                    49.96807008152752,
+                    18.62771543924463,
+                    49.96807008152752
+                ],
+                "temporal": [
+                    "2023-01-02T09:53:19Z",
+                    "2023-01-07T09:54:01Z"
+                ]
+            },
+            {
+                "file": "methaneset-s2-finetune_Russian_Federation.tacozip",
+                "id": "methaneset-s2-finetune",
+                "spatial": [
+                    39.73939260057195,
+                    46.73498436996476,
+                    39.73939260057195,
+                    46.73498436996476
+                ],
+                "temporal": [
+                    "2024-04-09T08:16:09Z",
+                    "2024-04-09T08:16:09Z"
+                ]
+            },
+            {
+                "file": "methaneset-s2-finetune_Saudi_Arabia.tacozip",
+                "id": "methaneset-s2-finetune",
+                "spatial": [
+                    47.362827470495674,
+                    25.13217158044472,
+                    47.362827470495674,
+                    25.13217158044472
+                ],
+                "temporal": [
+                    "2023-06-01T07:16:21Z",
+                    "2024-09-08T07:16:19Z"
+                ]
+            },
+            {
+                "file": "methaneset-s2-finetune_Uzbekistan.tacozip",
+                "id": "methaneset-s2-finetune",
+                "spatial": [
+                    63.469127128664816,
+                    38.47490313009119,
+                    66.01747912446798,
+                    40.21287308399348
+                ],
+                "temporal": [
+                    "2023-09-12T06:36:29Z",
+                    "2024-09-20T06:16:29Z"
+                ]
+            },
+            {
+                "file": "methaneset-s2-finetune_Venezuela.tacozip",
+                "id": "methaneset-s2-finetune",
+                "spatial": [
+                    -64.88379087490956,
+                    9.678564302139945,
+                    -63.474464193166334,
+                    10.079170779066825
+                ],
+                "temporal": [
+                    "2024-02-20T14:47:29Z",
+                    "2024-11-06T14:47:29Z"
+                ]
+            }
+        ]
+    }
+}

methaneset-s2-finetune/README.md

@@ -0,0 +1,189 @@
+
+# MethaneSET-S2 Finetune: Verified Methane Plume Events from Sentinel-2 for Supervised Learning
+
+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.
+## Dataset Information
+
+**Version**: 1.0.0
+
+**License**: CC-BY-4.0
+
+**Keywords**: methane, finetune, supervised, segmentation, plume-detection, remote-sensing, Sentinel-2, MSI, earth-observation, deep-learning
+
+**Tasks**: segmentation, classification, detection
+
+## Dataset Overview
+
+**Partitions**: 21 files
+**Spatial coverage**: [-103.98, -36.37, 116.52, 49.97] (WGS84)
+**Temporal coverage**: 2018-01-05 to 2024-12-30
+
+## Dataset Structure (Root-Sibling Uniform Tree)
+
+**Root**: FOLDER (3,612 samples)
+
+**Hierarchy**:
+
+- Level 1: FILE → FILE → FILE → FILE → FILE (18,060 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) |
+| `plume:geometry` | `binary` | Plume extent as WKB geometry |
+| `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-s2-finetune.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-s2-finetune.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-s2-finetune.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.
+
+---
+
+**DOI**: 10.1029/2014JD022685
+
+Lucchesi, R. (2013). GEOS-5 FP (Forward Processing) File Specification. NASA GMAO Technical Report.
+
+---
+
+### BibTeX
+
+```bibtex
+@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}
+}
+```
+
+---
+
+Generated with ❤️ using [TacoToolbox](https://github.com/tacotoolbox/tacotoolbox) v0.26.9

methaneset-s2-pretraining/.tacocat/COLLECTION.json

@@ -0,0 +1,1031 @@
+{
+    "id": "methaneset-s2-pretraining",
+    "dataset_version": "1.0.0",
+    "description": "methaneset-s2-pretraining is the plume-free subset of MethaneSET-S2, designed for self-supervised pretraining of methane detection models. This subset contains Sentinel-2 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 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, 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.",
+    "licenses": [
+        "CC-BY-4.0"
+    ],
+    "providers": [
+        {
+            "name": "UNEP IMEO",
+            "roles": [
+                "producer"
+            ],
+            "url": null,
+            "links": null
+        },
+        {
+            "name": "Source Cooperative",
+            "roles": [
+                "host"
+            ],
+            "url": null,
+            "links": null
+        }
+    ],
+    "tasks": [
+        "regression",
+        "classification",
+        "segmentation"
+    ],
+    "taco_version": "0.5.0",
+    "title": "MethaneSET-S2 Pretraining: Plume-Free Sentinel-2 Scenes for Self-Supervised Learning",
+    "curators": [
+        {
+            "name": "Cesar Aybar",
+            "organization": "Universitat de Val\u00e8ncia, Image and Signal Processing (ISP) Group",
+            "email": "cesar.aybar@uv.es",
+            "role": null
+        }
+    ],
+    "keywords": [
+        "methane",
+        "pretraining",
+        "self-supervised",
+        "remote-sensing",
+        "Sentinel-2",
+        "MSI",
+        "foundation-model",
+        "representation-learning",
+        "earth-observation",
+        "deep-learning"
+    ],
+    "extent": {
+        "spatial": [
+            -121.9053346285481,
+            -50.74620755918556,
+            151.41898292892918,
+            52.29623490526653
+        ],
+        "temporal": [
+            "2018-01-01T09:13:51Z",
+            "2024-12-31T17:06:29Z"
+        ]
+    },
+    "publications": [
+        {
+            "doi": "10.48550/arXiv.2411.15452",
+            "citation": "Vaughan, A.*, Mateo-Garcia, G.*, Irakulis-Loitxate, I., Watine, M., Fernandez-Poblaciones, P., Turner, R. E., Requeima, J., Gorro\u00f1o, J., Randles, C., Caltagirone, M., & Cifarelli, C.* (2024). AI for operational methane emitter monitoring from space. arXiv preprint arXiv:2411.15452.",
+            "summary": "Operational MARS-S2L system for global methane monitoring from Sentinel-2 and Landsat 8/9."
+        },
+        {
+            "doi": "10.48550/arXiv.2511.21777",
+            "citation": "Vaughan, A.*, Mateo-Garcia, G.*, Irakulis-Loitxate, I., Watine, M., Fernandez-Poblaciones, P., Turner, R. E., Requeima, J., Gorro\u00f1o, J., Randles, C., Caltagirone, M., & Cifarelli, C.* (2024). Artificial intelligence for methane detection: from continuous monitoring to verified mitigation. arXiv preprint arXiv:2511.21777.",
+            "summary": "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",
+            "citation": "Mu\u00f1oz-Sabater, J., et al. (2021). ERA5-Land: a state-of-the-art global reanalysis. Earth System Science Data, 13, 4349-4383.",
+            "summary": null
+        },
+        {
+            "doi": "10.1029/2014JD022685",
+            "citation": "Lucchesi, R. (2013). GEOS-5 FP (Forward Processing) File Specification. NASA GMAO Technical Report.",
+            "summary": null
+        }
+    ],
+    "taco:pit_schema": {
+        "root": {
+            "n": 57291,
+            "type": "FOLDER"
+        },
+        "shape": [
+            7136,
+            3
+        ],
+        "hierarchy": {
+            "1": [
+                {
+                    "n": 171873,
+                    "type": [
+                        "FILE",
+                        "FILE",
+                        "FILE"
+                    ],
+                    "id": [
+                        "target",
+                        "reference",
+                        "dem"
+                    ]
+                }
+            ]
+        }
+    },
+    "taco:field_schema": {
+        "level0": [
+            [
+                "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 (\u03bcs since Unix epoch, UTC)"
+            ],
+            [
+                "stac:centroid",
+                "binary",
+                "Center point in EPSG:4326 (WKB)"
+            ],
+            [
+                "stac:time_end",
+                "timestamp[us]",
+                "End timestamp (\u03bcs since Unix epoch, UTC)"
+            ],
+            [
+                "stac:time_middle",
+                "timestamp[us]",
+                "Middle timestamp (\u03bcs 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 \u00b0C 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": [
+            [
+                "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)."
+            ]
+        ]
+    },
+    "taco:sources": {
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+                "id": "methaneset-s2-pretraining",
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+                    39.73939260057195,
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+                    "2024-04-04T08:16:01Z"
+                ]
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+            {
+                "file": "methaneset-s2-pretraining_Tunisia.tacozip",
+                "id": "methaneset-s2-pretraining",
+                "spatial": [
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+                    "2024-10-16T10:00:29Z",
+                    "2024-11-30T10:03:51Z"
+                ]
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+            {
+                "file": "methaneset-s2-pretraining_Italy.tacozip",
+                "id": "methaneset-s2-pretraining",
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+                    7.693926808485255,
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+                    "2024-08-06T10:25:59Z"
+                ]
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+            {
+                "file": "methaneset-s2-pretraining_Turkey.tacozip",
+                "id": "methaneset-s2-pretraining",
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+                    "2024-12-22T08:12:49Z"
+                ]
+            }
+        ]
+    }
+}

methaneset-s2-pretraining/README.md

@@ -0,0 +1,188 @@
+
+# MethaneSET-S2 Pretraining: Plume-Free Sentinel-2 Scenes for Self-Supervised Learning
+
+methaneset-s2-pretraining is the plume-free subset of MethaneSET-S2, designed for self-supervised pretraining of methane detection models. This subset contains Sentinel-2 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 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, 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, Sentinel-2, MSI, foundation-model, representation-learning, earth-observation, deep-learning
+
+**Tasks**: regression, classification, segmentation
+
+## Dataset Overview
+
+**Partitions**: 42 files
+**Spatial coverage**: [-121.91, -50.75, 151.42, 52.30] (WGS84)
+**Temporal coverage**: 2018-01-01 to 2024-12-31
+
+## Dataset Structure (Root-Sibling Uniform Tree)
+
+**Root**: FOLDER (57,291 samples)
+
+**Hierarchy**:
+
+- Level 1: FILE → FILE → FILE (171,873 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-s2-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-s2-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-s2-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.
+
+---
+
+**DOI**: 10.1029/2014JD022685
+
+Lucchesi, R. (2013). GEOS-5 FP (Forward Processing) File Specification. NASA GMAO Technical Report.
+
+---
+
+### BibTeX
+
+```bibtex
+@dataset{methaneset-s2-pretraining1,
+  title = {MethaneSET-S2 Pretraining: Plume-Free Sentinel-2 Scenes for Self-Supervised Learning},
+  author = {Cesar Aybar},
+  year = {2018},
+  version = {1.0.0},
+  publisher = {Universitat de València, Image and Signal Processing (ISP) Group}
+}
+```
+
+---
+
+Generated with ❤️ using [TacoToolbox](https://github.com/tacotoolbox/tacotoolbox) v0.26.9