README

Index

The Major TOM Index is a global metadata catalog for the Major TOM grid at 10 km resolution. It provides a single entry point to discover, filter, and select tiles across sensors, locations, and time without downloading any imagery.

The index covers over 5 million tiles spanning the entire Earth. Each tile corresponds to a 1056 × 1056 px patch (10.56 × 10.56 km) aligned to Sentinel-2 MGRS tiles at 10 m resolution. Every tile is enriched with terrain, climate, soil, socioeconomic, and administrative attributes derived from public Earth Engine datasets.

What can you do with this index?

Find tiles by location. Filter by country, state, MGRS tile code, or bounding box using the GeoParquet geometry column.
Select tiles by environmental criteria. Want arid, high-elevation tiles? Filter by climate:precipitation < 200 and terrain:elevation > 3000.
Stratify sampling for training sets. Use the enrichment columns to build geographically and environmentally balanced splits for foundation model pretraining.
Link to imagery. The land_s2 and land_l8 files include sensor-specific image IDs (s2:id_gee, l8:id_gee) that point directly to the source products in Google Earth Engine.
Use the ELLIOT splits. The elliot.parquet file provides pre-built monotemporal and temporal splits designed for multi-sensor, multi-temporal EO research.

All files are self-contained GeoParquet with ZSTD compression, sorted by majortom:code_1000km → majortom:code_100km → id for efficient spatial predicate pushdown.

Schema

Columns are organized into namespaces. Each namespace groups related attributes.

Grid (`majortom:`)

Tile identity and spatial reference within the Major TOM grid system.

Column	Type	Description
`id`	string	Unique tile identifier (e.g. `MT10_770U_395R`).
`majortom:code_100km`	string	Parent 100 km grid cell. Used for spatial grouping.
`majortom:code_1000km`	string	Parent 1000 km grid cell. Used for coarse-level partitioning.
`majortom:crs`	string	Native UTM CRS of the tile (e.g. `EPSG:32647`).
`majortom:mgrs_tile`	string	MGRS tile code (e.g. `47WNS`). Links to Sentinel-2 tiling grid.
`majortom:mgrs_n`	uint8	Number of overlapping MGRS tiles (1 after deduplication).
`majortom:mgrs_candidates`	list<string>	All candidate MGRS tiles before deduplication.
`majortom:footprint_pct`	float	Percentage of tile covered by the assigned MGRS tile.
`majortom:geotransform`	list<int32>	Snapped affine geotransform [originX, scaleX, shearX, originY, shearY, scaleY].
`majortom:geotransform_raw`	list<double>	Original (unsnapped) affine geotransform.

STAC (`stac:`)

Spatial and temporal reference following STAC conventions. Present in land_s2 and land_l8 only, where it replaces the majortom: grid columns.

Column	Type	Description
`stac:crs`	string	Coordinate reference system.
`stac:geotransform`	list<int64>	Affine geotransform for the image patch.
`stac:tensor_shape`	list<int32>	Shape of the image tensor [bands, height, width].
`stac:time_start`	int64	Acquisition start time (Unix timestamp).
`stac:time_end`	int64	Acquisition end time (Unix timestamp).

Sentinel-2 (`s2:`)

Sensor metadata for the assigned Sentinel-2 image. Present in land_s2 only.

Column	Type	Description
`s2:id_gee`	string	Google Earth Engine image ID. Use this to fetch the actual imagery.
`s2:product_id`	string	ESA product identifier.
`s2:spacecraft`	string	Spacecraft name (Sentinel-2A or Sentinel-2B).
`s2:processing_baseline`	string	Processing baseline version.
`s2:orbit_number`	uint16	Relative orbit number.
`s2:mean_solar_azimuth`	float	Mean solar azimuth angle, averaged across all bands and detectors (degrees).
`s2:mean_solar_zenith`	float	Mean solar zenith angle, averaged across all bands and detectors (degrees).
`s2:mean_view_azimuth`	float	Mean viewing azimuth angle from band B8 (degrees).
`s2:mean_view_zenith`	float	Mean viewing zenith angle from band B8 (degrees).
`s2:reflectance_conversion`	float	Reflectance conversion factor (U correction).

Note on solar vs viewing angles. The sun has a single position relative to the scene, so ESA provides one solar azimuth and one solar zenith averaged across all bands. Viewing angles are different: Sentinel-2 uses a pushbroom sensor where each spectral band has its own detector array in the focal plane, each observing from a slightly different angle. That is why GEE provides per-band viewing angles (MEAN_INCIDENCE_*_ANGLE_B1 through _B12). We use band B8 (NIR, 10 m) as the reference because it is at native 10 m resolution and sits near the center of the focal plane, making it a representative proxy for the viewing geometry of the 10 m and 20 m bands.

Landsat 8/9 (`l8:`)

Sensor metadata for the assigned Landsat image. Present in land_l8 only.

Column	Type	Description
`l8:id_gee`	string	Google Earth Engine image ID. Use this to fetch the actual imagery.
`l8:product_id`	string	USGS product identifier.
`l8:spacecraft`	string	Spacecraft name (Landsat 8 or Landsat 9).
`l8:collection_number`	uint8	USGS Collection number.
`l8:collection_category`	string	Collection category (T1, T2, RT).
`l8:processing_software`	string	Processing software version.
`l8:wrs_path`	uint16	WRS-2 path number.
`l8:wrs_row`	uint16	WRS-2 row number.
`l8:cloud_cover`	float	Scene cloud cover percentage.
`l8:sun_azimuth`	float	Sun azimuth angle (degrees).
`l8:sun_elevation`	float	Sun elevation angle (degrees).
`l8:earth_sun_distance`	float	Earth-Sun distance (astronomical units).
`l8:image_quality_oli`	uint8	OLI image quality score.
`l8:roll_angle`	float	Spacecraft roll angle (degrees).

Terrain (`terrain:`)

Column	Type	Range	Description
`terrain:elevation`	float	~-420 to 8,849 (m)	Mean elevation in meters from the Copernicus GLO-30 DEM, a 30 m resolution Digital Surface Model derived from TanDEM-X radar satellite data (2011 to 2015). Includes buildings, infrastructure, and vegetation. Uses the EGM2008 vertical datum.

Climate (`climate:`)

Column	Type	Range	Description
`climate:precipitation`	float	0+ (mm/year)	Mean annual precipitation estimated from GPM (Global Precipitation Measurement) satellite data, aggregated as a long-term annual mean.
`climate:temperature`	float	~-40 to 50 (°C)	Mean annual land surface temperature estimated from MODIS LST satellite data, aggregated as a long-term annual mean.

Soil (`soil:`)

Surface-layer soil properties from the OpenLandMap dataset, derived from machine learning predictions on global soil survey data at 250 m resolution.

Column	Type	Range	Description
`soil:clay`	float	0 to 100 (%)	Clay content weight fraction at 0 cm depth. Source.
`soil:sand`	float	0 to 100 (%)	Sand content weight fraction at 0 cm depth. Source.
`soil:carbon`	float	0+ (g/kg)	Soil organic carbon content at 0 cm depth. Source.
`soil:bulk_density`	float	0+ (kg/m³)	Fine-earth bulk density at 0 cm depth. Source.
`soil:ph`	float	~3 to 10	Soil pH in water at 0 cm depth. Source.

Socioeconomic (`socio:`)

Column	Type	Range	Description
`socio:gdp`	float	0+ (USD)	GDP per capita at purchasing power parity (PPP, constant 2021 USD) for the year 2022. From the Kummu et al. (2025) gridded dataset, downscaled to admin-2 level (43,501 units) at 5 arc-min resolution. GEE catalog.
`socio:population`	float	0+ (people)	Estimated number of people per grid cell from the Meta High Resolution Settlement Layer (HRSL). Uses satellite imagery and census data at ~30 m resolution.
`socio:human_modification`	float	0.0 to 1.0	Cumulative degree of human modification of terrestrial ecosystems from the Global Human Modification v3 (Theobald et al. 2025). Combines the spatial footprint and intensity of 13 stressors across five categories: settlement, agriculture, transportation, mining/energy, and electrical infrastructure. 0 = no modification, 1 = fully modified. 300 m resolution. GEE catalog.
`socio:cisi`	float	0.0 to 1.0	Critical Infrastructure Spatial Index (Nirandjan et al. 2022). Aggregates OpenStreetMap data on 39 types of critical infrastructure across seven systems: transportation, energy, telecommunication, waste, water, education, and health. 0 = no infrastructure, 1 = highest density. 0.10° resolution. GEE catalog.

Administrative (`admin:`)

Human-readable administrative boundary names resolved from rasterized boundary datasets.

Column	Type	Description
`admin:country`	string	Country name. Tiles over ocean/lakes are labeled `Ocean/Sea/Lakes`.
`admin:state`	string	State or province name.
`admin:district`	string	District or county name.

Other

Column	Type	Description
`geometry`	binary (WKB)	Tile geometry. All files include GeoParquet metadata for spatial queries.
`split`	string	ELLIOT split assignment: `monotemporal` or `temporal`. Present in `elliot.parquet` only.

Files

File	Rows	Columns	Size	Description
`global.parquet`	5,055,204	26	146 MB	Every 10 km tile on Earth. The complete grid with all enrichment columns.
`land.parquet`	2,767,104	26	91 MB	Tiles covered by land-observing sensors (Sentinel-2 and Landsat). Same schema as global.
`land_s2.parquet`	2,547,253	34	127 MB	Land tiles with a Sentinel-2 image assigned. Adds `stac:` and `s2:` sensor metadata.
`land_l8.parquet`	2,255,537	38	97 MB	Land tiles with a Landsat 8/9 image assigned. Adds `stac:` and `l8:` sensor metadata.
`elliot.parquet`	279,166	27	14 MB	ELLIOT subset with monotemporal and temporal split assignments. Same enrichment as global plus `split` column.

Namespace availability per file

Namespace	global	land	land_s2	land_l8	elliot
`majortom:`	✓	✓			✓
`stac:`			✓	✓
`s2:`			✓
`l8:`				✓
`terrain:`	✓	✓	✓	✓	✓
`climate:`	✓	✓	✓	✓	✓
`soil:`	✓	✓	✓	✓	✓
`socio:`	✓	✓	✓	✓	✓
`admin:`	✓	✓	✓	✓	✓
`split`					✓
`geometry`	✓	✓	✓	✓	✓

Quick Start

DuckDB

1import duckdb
2
3duckdb.sql("""
4INSTALL spatial;
5INSTALL httpfs;
6LOAD spatial;
7LOAD httpfs;
8
9SELECT "admin:country", COUNT(*) as n_tiles
10FROM 'https://data.source.coop/major-tom/index/land_s2.parquet'
11WHERE "admin:country" IN ('Peru', 'Brazil', 'Colombia', 'Chile', 'Argentina')
12GROUP BY "admin:country"
13ORDER BY n_tiles DESC;
14"""
1import duckdb
2
3duckdb.sql("""
4INSTALL spatial;
5INSTALL httpfs;
6LOAD spatial;
7LOAD httpfs;
8
9SELECT "admin:country", COUNT(*) as n_tiles
10FROM 'https://data.source.coop/major-tom/index/land_s2.parquet'
11WHERE "admin:country" IN ('Peru', 'Brazil', 'Colombia', 'Chile', 'Argentina')
12GROUP BY "admin:country"
13ORDER BY n_tiles DESC;
14"""

Pandas

1import pandas as pd
2import requests
3from io import BytesIO
4
5BASE = "https://data.source.coop/major-tom/index"
6
7# Load land tiles with Sentinel-2 metadata
8r = requests.get(f"{BASE}/land_s2.parquet")
9df = pd.read_parquet(BytesIO(r.content))
10
1import pandas as pd
2import requests
3from io import BytesIO
4
5BASE = "https://data.source.coop/major-tom/index"
6
7# Load land tiles with Sentinel-2 metadata
8r = requests.get(f"{BASE}/land_s2.parquet")
9df = pd.read_parquet(BytesIO(r.content))
10

ELLIOT Splits

The elliot.parquet file contains 279,166 tiles selected for the ELLIOT project multi-temporal dataset extension. Tile locations were sampled using hierarchical spherical k-means (530 × 528 = 279,840 clusters) over AlphaEarth Foundation embeddings to ensure global environmental diversity.

The split column defines two subsets:

Monotemporal (250,000 tiles). One cloud-free image per sensor per location. Designed for tasks where spatial coverage matters more than temporal depth: land cover classification, feature extraction, or pretraining foundation models on diverse global scenes.
Temporal (29,166 tiles). Multiple observations per location across time. Designed for tasks that require temporal context: change detection, phenology tracking, seasonal compositing, or training models that learn from multi-temporal sequences. This subset is further divided into monthly cadence (12,500 tiles × 12 timesteps) and five-daily cadence (16,666 tiles × 6 timesteps).

Citation

1@inproceedings{Francis2024MajorTOM,
2    author    = {Francis, Alistair and Czerkawski, Mikolaj},
3    title     = {Major TOM: Expandable Datasets for Earth Observation},
4    booktitle = {IGARSS 2024 - IEEE International Geoscience and Remote Sensing Symposium},
5    year      = {2024},
6    pages     = {2935--2940},
7    doi       = {10.1109/IGARSS53475.2024.10640760}
8}
1@inproceedings{Francis2024MajorTOM,
2    author    = {Francis, Alistair and Czerkawski, Mikolaj},
3    title     = {Major TOM: Expandable Datasets for Earth Observation},
4    booktitle = {IGARSS 2024 - IEEE International Geoscience and Remote Sensing Symposium},
5    year      = {2024},
6    pages     = {2935--2940},
7    doi       = {10.1109/IGARSS53475.2024.10640760}
8}

License

CC-BY-4.0

Acknowledgements

The Major TOM Index has been made possible thanks to Asterisk Labs, the ELLIOT project (European Commission, Horizon Europe, Grant 101214398), and the Image and Signal Processing Group (ISP) at Universitat de València.

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Download

Index

What can you do with this index?

Find tiles by location. Filter by country, state, MGRS tile code, or bounding box using the GeoParquet geometry column.
Select tiles by environmental criteria. Want arid, high-elevation tiles? Filter by climate:precipitation < 200 and terrain:elevation > 3000.
Stratify sampling for training sets. Use the enrichment columns to build geographically and environmentally balanced splits for foundation model pretraining.
Link to imagery. The land_s2 and land_l8 files include sensor-specific image IDs (s2:id_gee, l8:id_gee) that point directly to the source products in Google Earth Engine.
Use the ELLIOT splits. The elliot.parquet file provides pre-built monotemporal and temporal splits designed for multi-sensor, multi-temporal EO research.

All files are self-contained GeoParquet with ZSTD compression, sorted by majortom:code_1000km → majortom:code_100km → id for efficient spatial predicate pushdown.

Schema

Columns are organized into namespaces. Each namespace groups related attributes.

Grid (`majortom:`)

Tile identity and spatial reference within the Major TOM grid system.

Column	Type	Description
`id`	string	Unique tile identifier (e.g. `MT10_770U_395R`).
`majortom:code_100km`	string	Parent 100 km grid cell. Used for spatial grouping.
`majortom:code_1000km`	string	Parent 1000 km grid cell. Used for coarse-level partitioning.
`majortom:crs`	string	Native UTM CRS of the tile (e.g. `EPSG:32647`).
`majortom:mgrs_tile`	string	MGRS tile code (e.g. `47WNS`). Links to Sentinel-2 tiling grid.
`majortom:mgrs_n`	uint8	Number of overlapping MGRS tiles (1 after deduplication).
`majortom:mgrs_candidates`	list<string>	All candidate MGRS tiles before deduplication.
`majortom:footprint_pct`	float	Percentage of tile covered by the assigned MGRS tile.
`majortom:geotransform`	list<int32>	Snapped affine geotransform [originX, scaleX, shearX, originY, shearY, scaleY].
`majortom:geotransform_raw`	list<double>	Original (unsnapped) affine geotransform.

STAC (`stac:`)

Spatial and temporal reference following STAC conventions. Present in land_s2 and land_l8 only, where it replaces the majortom: grid columns.

Column	Type	Description
`stac:crs`	string	Coordinate reference system.
`stac:geotransform`	list<int64>	Affine geotransform for the image patch.
`stac:tensor_shape`	list<int32>	Shape of the image tensor [bands, height, width].
`stac:time_start`	int64	Acquisition start time (Unix timestamp).
`stac:time_end`	int64	Acquisition end time (Unix timestamp).

Sentinel-2 (`s2:`)

Sensor metadata for the assigned Sentinel-2 image. Present in land_s2 only.

Column	Type	Description
`s2:id_gee`	string	Google Earth Engine image ID. Use this to fetch the actual imagery.
`s2:product_id`	string	ESA product identifier.
`s2:spacecraft`	string	Spacecraft name (Sentinel-2A or Sentinel-2B).
`s2:processing_baseline`	string	Processing baseline version.
`s2:orbit_number`	uint16	Relative orbit number.
`s2:mean_solar_azimuth`	float	Mean solar azimuth angle, averaged across all bands and detectors (degrees).
`s2:mean_solar_zenith`	float	Mean solar zenith angle, averaged across all bands and detectors (degrees).
`s2:mean_view_azimuth`	float	Mean viewing azimuth angle from band B8 (degrees).
`s2:mean_view_zenith`	float	Mean viewing zenith angle from band B8 (degrees).
`s2:reflectance_conversion`	float	Reflectance conversion factor (U correction).

Note on solar vs viewing angles. The sun has a single position relative to the scene, so ESA provides one solar azimuth and one solar zenith averaged across all bands. Viewing angles are different: Sentinel-2 uses a pushbroom sensor where each spectral band has its own detector array in the focal plane, each observing from a slightly different angle. That is why GEE provides per-band viewing angles (MEAN_INCIDENCE_*_ANGLE_B1 through _B12). We use band B8 (NIR, 10 m) as the reference because it is at native 10 m resolution and sits near the center of the focal plane, making it a representative proxy for the viewing geometry of the 10 m and 20 m bands.

Landsat 8/9 (`l8:`)

Sensor metadata for the assigned Landsat image. Present in land_l8 only.

Column	Type	Description
`l8:id_gee`	string	Google Earth Engine image ID. Use this to fetch the actual imagery.
`l8:product_id`	string	USGS product identifier.
`l8:spacecraft`	string	Spacecraft name (Landsat 8 or Landsat 9).
`l8:collection_number`	uint8	USGS Collection number.
`l8:collection_category`	string	Collection category (T1, T2, RT).
`l8:processing_software`	string	Processing software version.
`l8:wrs_path`	uint16	WRS-2 path number.
`l8:wrs_row`	uint16	WRS-2 row number.
`l8:cloud_cover`	float	Scene cloud cover percentage.
`l8:sun_azimuth`	float	Sun azimuth angle (degrees).
`l8:sun_elevation`	float	Sun elevation angle (degrees).
`l8:earth_sun_distance`	float	Earth-Sun distance (astronomical units).
`l8:image_quality_oli`	uint8	OLI image quality score.
`l8:roll_angle`	float	Spacecraft roll angle (degrees).

Terrain (`terrain:`)

Column	Type	Range	Description
`terrain:elevation`	float	~-420 to 8,849 (m)	Mean elevation in meters from the Copernicus GLO-30 DEM, a 30 m resolution Digital Surface Model derived from TanDEM-X radar satellite data (2011 to 2015). Includes buildings, infrastructure, and vegetation. Uses the EGM2008 vertical datum.

Climate (`climate:`)

Column	Type	Range	Description
`climate:precipitation`	float	0+ (mm/year)	Mean annual precipitation estimated from GPM (Global Precipitation Measurement) satellite data, aggregated as a long-term annual mean.
`climate:temperature`	float	~-40 to 50 (°C)	Mean annual land surface temperature estimated from MODIS LST satellite data, aggregated as a long-term annual mean.

Soil (`soil:`)

Surface-layer soil properties from the OpenLandMap dataset, derived from machine learning predictions on global soil survey data at 250 m resolution.

Column	Type	Range	Description
`soil:clay`	float	0 to 100 (%)	Clay content weight fraction at 0 cm depth. Source.
`soil:sand`	float	0 to 100 (%)	Sand content weight fraction at 0 cm depth. Source.
`soil:carbon`	float	0+ (g/kg)	Soil organic carbon content at 0 cm depth. Source.
`soil:bulk_density`	float	0+ (kg/m³)	Fine-earth bulk density at 0 cm depth. Source.
`soil:ph`	float	~3 to 10	Soil pH in water at 0 cm depth. Source.

Socioeconomic (`socio:`)

Column	Type	Range	Description
`socio:gdp`	float	0+ (USD)	GDP per capita at purchasing power parity (PPP, constant 2021 USD) for the year 2022. From the Kummu et al. (2025) gridded dataset, downscaled to admin-2 level (43,501 units) at 5 arc-min resolution. GEE catalog.
`socio:population`	float	0+ (people)	Estimated number of people per grid cell from the Meta High Resolution Settlement Layer (HRSL). Uses satellite imagery and census data at ~30 m resolution.
`socio:human_modification`	float	0.0 to 1.0	Cumulative degree of human modification of terrestrial ecosystems from the Global Human Modification v3 (Theobald et al. 2025). Combines the spatial footprint and intensity of 13 stressors across five categories: settlement, agriculture, transportation, mining/energy, and electrical infrastructure. 0 = no modification, 1 = fully modified. 300 m resolution. GEE catalog.
`socio:cisi`	float	0.0 to 1.0	Critical Infrastructure Spatial Index (Nirandjan et al. 2022). Aggregates OpenStreetMap data on 39 types of critical infrastructure across seven systems: transportation, energy, telecommunication, waste, water, education, and health. 0 = no infrastructure, 1 = highest density. 0.10° resolution. GEE catalog.

Administrative (`admin:`)

Human-readable administrative boundary names resolved from rasterized boundary datasets.

Column	Type	Description
`admin:country`	string	Country name. Tiles over ocean/lakes are labeled `Ocean/Sea/Lakes`.
`admin:state`	string	State or province name.
`admin:district`	string	District or county name.

Other

Column	Type	Description
`geometry`	binary (WKB)	Tile geometry. All files include GeoParquet metadata for spatial queries.
`split`	string	ELLIOT split assignment: `monotemporal` or `temporal`. Present in `elliot.parquet` only.

Files

File	Rows	Columns	Size	Description
`global.parquet`	5,055,204	26	146 MB	Every 10 km tile on Earth. The complete grid with all enrichment columns.
`land.parquet`	2,767,104	26	91 MB	Tiles covered by land-observing sensors (Sentinel-2 and Landsat). Same schema as global.
`land_s2.parquet`	2,547,253	34	127 MB	Land tiles with a Sentinel-2 image assigned. Adds `stac:` and `s2:` sensor metadata.
`land_l8.parquet`	2,255,537	38	97 MB	Land tiles with a Landsat 8/9 image assigned. Adds `stac:` and `l8:` sensor metadata.
`elliot.parquet`	279,166	27	14 MB	ELLIOT subset with monotemporal and temporal split assignments. Same enrichment as global plus `split` column.

Namespace availability per file

Namespace	global	land	land_s2	land_l8	elliot
`majortom:`	✓	✓			✓
`stac:`			✓	✓
`s2:`			✓
`l8:`				✓
`terrain:`	✓	✓	✓	✓	✓
`climate:`	✓	✓	✓	✓	✓
`soil:`	✓	✓	✓	✓	✓
`socio:`	✓	✓	✓	✓	✓
`admin:`	✓	✓	✓	✓	✓
`split`					✓
`geometry`	✓	✓	✓	✓	✓

Quick Start

DuckDB

Pandas

ELLIOT Splits

The split column defines two subsets:

Monotemporal (250,000 tiles). One cloud-free image per sensor per location. Designed for tasks where spatial coverage matters more than temporal depth: land cover classification, feature extraction, or pretraining foundation models on diverse global scenes.
Temporal (29,166 tiles). Multiple observations per location across time. Designed for tasks that require temporal context: change detection, phenology tracking, seasonal compositing, or training models that learn from multi-temporal sequences. This subset is further divided into monthly cadence (12,500 tiles × 12 timesteps) and five-daily cadence (16,666 tiles × 6 timesteps).

Citation

License

CC-BY-4.0

Acknowledgements

README

Index

What can you do with this index?

Find tiles by location. Filter by country, state, MGRS tile code, or bounding box using the GeoParquet geometry column.
Select tiles by environmental criteria. Want arid, high-elevation tiles? Filter by climate:precipitation < 200 and terrain:elevation > 3000.
Stratify sampling for training sets. Use the enrichment columns to build geographically and environmentally balanced splits for foundation model pretraining.
Link to imagery. The land_s2 and land_l8 files include sensor-specific image IDs (s2:id_gee, l8:id_gee) that point directly to the source products in Google Earth Engine.
Use the ELLIOT splits. The elliot.parquet file provides pre-built monotemporal and temporal splits designed for multi-sensor, multi-temporal EO research.

All files are self-contained GeoParquet with ZSTD compression, sorted by majortom:code_1000km → majortom:code_100km → id for efficient spatial predicate pushdown.

Schema

Columns are organized into namespaces. Each namespace groups related attributes.

Grid (`majortom:`)

Tile identity and spatial reference within the Major TOM grid system.

Column	Type	Description
`id`	string	Unique tile identifier (e.g. `MT10_770U_395R`).
`majortom:code_100km`	string	Parent 100 km grid cell. Used for spatial grouping.
`majortom:code_1000km`	string	Parent 1000 km grid cell. Used for coarse-level partitioning.
`majortom:crs`	string	Native UTM CRS of the tile (e.g. `EPSG:32647`).
`majortom:mgrs_tile`	string	MGRS tile code (e.g. `47WNS`). Links to Sentinel-2 tiling grid.
`majortom:mgrs_n`	uint8	Number of overlapping MGRS tiles (1 after deduplication).
`majortom:mgrs_candidates`	list<string>	All candidate MGRS tiles before deduplication.
`majortom:footprint_pct`	float	Percentage of tile covered by the assigned MGRS tile.
`majortom:geotransform`	list<int32>	Snapped affine geotransform [originX, scaleX, shearX, originY, shearY, scaleY].
`majortom:geotransform_raw`	list<double>	Original (unsnapped) affine geotransform.

STAC (`stac:`)

Spatial and temporal reference following STAC conventions. Present in land_s2 and land_l8 only, where it replaces the majortom: grid columns.

Column	Type	Description
`stac:crs`	string	Coordinate reference system.
`stac:geotransform`	list<int64>	Affine geotransform for the image patch.
`stac:tensor_shape`	list<int32>	Shape of the image tensor [bands, height, width].
`stac:time_start`	int64	Acquisition start time (Unix timestamp).
`stac:time_end`	int64	Acquisition end time (Unix timestamp).

Sentinel-2 (`s2:`)

Sensor metadata for the assigned Sentinel-2 image. Present in land_s2 only.

Column	Type	Description
`s2:id_gee`	string	Google Earth Engine image ID. Use this to fetch the actual imagery.
`s2:product_id`	string	ESA product identifier.
`s2:spacecraft`	string	Spacecraft name (Sentinel-2A or Sentinel-2B).
`s2:processing_baseline`	string	Processing baseline version.
`s2:orbit_number`	uint16	Relative orbit number.
`s2:mean_solar_azimuth`	float	Mean solar azimuth angle, averaged across all bands and detectors (degrees).
`s2:mean_solar_zenith`	float	Mean solar zenith angle, averaged across all bands and detectors (degrees).
`s2:mean_view_azimuth`	float	Mean viewing azimuth angle from band B8 (degrees).
`s2:mean_view_zenith`	float	Mean viewing zenith angle from band B8 (degrees).
`s2:reflectance_conversion`	float	Reflectance conversion factor (U correction).

Note on solar vs viewing angles. The sun has a single position relative to the scene, so ESA provides one solar azimuth and one solar zenith averaged across all bands. Viewing angles are different: Sentinel-2 uses a pushbroom sensor where each spectral band has its own detector array in the focal plane, each observing from a slightly different angle. That is why GEE provides per-band viewing angles (MEAN_INCIDENCE_*_ANGLE_B1 through _B12). We use band B8 (NIR, 10 m) as the reference because it is at native 10 m resolution and sits near the center of the focal plane, making it a representative proxy for the viewing geometry of the 10 m and 20 m bands.

Landsat 8/9 (`l8:`)

Sensor metadata for the assigned Landsat image. Present in land_l8 only.

Column	Type	Description
`l8:id_gee`	string	Google Earth Engine image ID. Use this to fetch the actual imagery.
`l8:product_id`	string	USGS product identifier.
`l8:spacecraft`	string	Spacecraft name (Landsat 8 or Landsat 9).
`l8:collection_number`	uint8	USGS Collection number.
`l8:collection_category`	string	Collection category (T1, T2, RT).
`l8:processing_software`	string	Processing software version.
`l8:wrs_path`	uint16	WRS-2 path number.
`l8:wrs_row`	uint16	WRS-2 row number.
`l8:cloud_cover`	float	Scene cloud cover percentage.
`l8:sun_azimuth`	float	Sun azimuth angle (degrees).
`l8:sun_elevation`	float	Sun elevation angle (degrees).
`l8:earth_sun_distance`	float	Earth-Sun distance (astronomical units).
`l8:image_quality_oli`	uint8	OLI image quality score.
`l8:roll_angle`	float	Spacecraft roll angle (degrees).

Terrain (`terrain:`)

Column	Type	Range	Description
`terrain:elevation`	float	~-420 to 8,849 (m)	Mean elevation in meters from the Copernicus GLO-30 DEM, a 30 m resolution Digital Surface Model derived from TanDEM-X radar satellite data (2011 to 2015). Includes buildings, infrastructure, and vegetation. Uses the EGM2008 vertical datum.

Climate (`climate:`)

Column	Type	Range	Description
`climate:precipitation`	float	0+ (mm/year)	Mean annual precipitation estimated from GPM (Global Precipitation Measurement) satellite data, aggregated as a long-term annual mean.
`climate:temperature`	float	~-40 to 50 (°C)	Mean annual land surface temperature estimated from MODIS LST satellite data, aggregated as a long-term annual mean.

Soil (`soil:`)

Surface-layer soil properties from the OpenLandMap dataset, derived from machine learning predictions on global soil survey data at 250 m resolution.

Column	Type	Range	Description
`soil:clay`	float	0 to 100 (%)	Clay content weight fraction at 0 cm depth. Source.
`soil:sand`	float	0 to 100 (%)	Sand content weight fraction at 0 cm depth. Source.
`soil:carbon`	float	0+ (g/kg)	Soil organic carbon content at 0 cm depth. Source.
`soil:bulk_density`	float	0+ (kg/m³)	Fine-earth bulk density at 0 cm depth. Source.
`soil:ph`	float	~3 to 10	Soil pH in water at 0 cm depth. Source.

Socioeconomic (`socio:`)

Column	Type	Range	Description
`socio:gdp`	float	0+ (USD)	GDP per capita at purchasing power parity (PPP, constant 2021 USD) for the year 2022. From the Kummu et al. (2025) gridded dataset, downscaled to admin-2 level (43,501 units) at 5 arc-min resolution. GEE catalog.
`socio:population`	float	0+ (people)	Estimated number of people per grid cell from the Meta High Resolution Settlement Layer (HRSL). Uses satellite imagery and census data at ~30 m resolution.
`socio:human_modification`	float	0.0 to 1.0	Cumulative degree of human modification of terrestrial ecosystems from the Global Human Modification v3 (Theobald et al. 2025). Combines the spatial footprint and intensity of 13 stressors across five categories: settlement, agriculture, transportation, mining/energy, and electrical infrastructure. 0 = no modification, 1 = fully modified. 300 m resolution. GEE catalog.
`socio:cisi`	float	0.0 to 1.0	Critical Infrastructure Spatial Index (Nirandjan et al. 2022). Aggregates OpenStreetMap data on 39 types of critical infrastructure across seven systems: transportation, energy, telecommunication, waste, water, education, and health. 0 = no infrastructure, 1 = highest density. 0.10° resolution. GEE catalog.

Administrative (`admin:`)

Human-readable administrative boundary names resolved from rasterized boundary datasets.

Column	Type	Description
`admin:country`	string	Country name. Tiles over ocean/lakes are labeled `Ocean/Sea/Lakes`.
`admin:state`	string	State or province name.
`admin:district`	string	District or county name.

Other

Column	Type	Description
`geometry`	binary (WKB)	Tile geometry. All files include GeoParquet metadata for spatial queries.
`split`	string	ELLIOT split assignment: `monotemporal` or `temporal`. Present in `elliot.parquet` only.

Files

File	Rows	Columns	Size	Description
`global.parquet`	5,055,204	26	146 MB	Every 10 km tile on Earth. The complete grid with all enrichment columns.
`land.parquet`	2,767,104	26	91 MB	Tiles covered by land-observing sensors (Sentinel-2 and Landsat). Same schema as global.
`land_s2.parquet`	2,547,253	34	127 MB	Land tiles with a Sentinel-2 image assigned. Adds `stac:` and `s2:` sensor metadata.
`land_l8.parquet`	2,255,537	38	97 MB	Land tiles with a Landsat 8/9 image assigned. Adds `stac:` and `l8:` sensor metadata.
`elliot.parquet`	279,166	27	14 MB	ELLIOT subset with monotemporal and temporal split assignments. Same enrichment as global plus `split` column.

Namespace availability per file

Namespace	global	land	land_s2	land_l8	elliot
`majortom:`	✓	✓			✓
`stac:`			✓	✓
`s2:`			✓
`l8:`				✓
`terrain:`	✓	✓	✓	✓	✓
`climate:`	✓	✓	✓	✓	✓
`soil:`	✓	✓	✓	✓	✓
`socio:`	✓	✓	✓	✓	✓
`admin:`	✓	✓	✓	✓	✓
`split`					✓
`geometry`	✓	✓	✓	✓	✓

Quick Start

DuckDB

1import duckdb
2
3duckdb.sql("""
4INSTALL spatial;
5INSTALL httpfs;
6LOAD spatial;
7LOAD httpfs;
8
9SELECT "admin:country", COUNT(*) as n_tiles
10FROM 'https://data.source.coop/major-tom/index/land_s2.parquet'
11WHERE "admin:country" IN ('Peru', 'Brazil', 'Colombia', 'Chile', 'Argentina')
12GROUP BY "admin:country"
13ORDER BY n_tiles DESC;
14"""
1import duckdb
2
3duckdb.sql("""
4INSTALL spatial;
5INSTALL httpfs;
6LOAD spatial;
7LOAD httpfs;
8
9SELECT "admin:country", COUNT(*) as n_tiles
10FROM 'https://data.source.coop/major-tom/index/land_s2.parquet'
11WHERE "admin:country" IN ('Peru', 'Brazil', 'Colombia', 'Chile', 'Argentina')
12GROUP BY "admin:country"
13ORDER BY n_tiles DESC;
14"""

Pandas

1import pandas as pd
2import requests
3from io import BytesIO
4
5BASE = "https://data.source.coop/major-tom/index"
6
7# Load land tiles with Sentinel-2 metadata
8r = requests.get(f"{BASE}/land_s2.parquet")
9df = pd.read_parquet(BytesIO(r.content))
10
1import pandas as pd
2import requests
3from io import BytesIO
4
5BASE = "https://data.source.coop/major-tom/index"
6
7# Load land tiles with Sentinel-2 metadata
8r = requests.get(f"{BASE}/land_s2.parquet")
9df = pd.read_parquet(BytesIO(r.content))
10

ELLIOT Splits

The split column defines two subsets:

Monotemporal (250,000 tiles). One cloud-free image per sensor per location. Designed for tasks where spatial coverage matters more than temporal depth: land cover classification, feature extraction, or pretraining foundation models on diverse global scenes.
Temporal (29,166 tiles). Multiple observations per location across time. Designed for tasks that require temporal context: change detection, phenology tracking, seasonal compositing, or training models that learn from multi-temporal sequences. This subset is further divided into monthly cadence (12,500 tiles × 12 timesteps) and five-daily cadence (16,666 tiles × 6 timesteps).

Citation

1@inproceedings{Francis2024MajorTOM,
2    author    = {Francis, Alistair and Czerkawski, Mikolaj},
3    title     = {Major TOM: Expandable Datasets for Earth Observation},
4    booktitle = {IGARSS 2024 - IEEE International Geoscience and Remote Sensing Symposium},
5    year      = {2024},
6    pages     = {2935--2940},
7    doi       = {10.1109/IGARSS53475.2024.10640760}
8}
1@inproceedings{Francis2024MajorTOM,
2    author    = {Francis, Alistair and Czerkawski, Mikolaj},
3    title     = {Major TOM: Expandable Datasets for Earth Observation},
4    booktitle = {IGARSS 2024 - IEEE International Geoscience and Remote Sensing Symposium},
5    year      = {2024},
6    pages     = {2935--2940},
7    doi       = {10.1109/IGARSS53475.2024.10640760}
8}

License

CC-BY-4.0

Acknowledgements

Index

What can you do with this index?

Find tiles by location. Filter by country, state, MGRS tile code, or bounding box using the GeoParquet geometry column.
Select tiles by environmental criteria. Want arid, high-elevation tiles? Filter by climate:precipitation < 200 and terrain:elevation > 3000.
Stratify sampling for training sets. Use the enrichment columns to build geographically and environmentally balanced splits for foundation model pretraining.
Link to imagery. The land_s2 and land_l8 files include sensor-specific image IDs (s2:id_gee, l8:id_gee) that point directly to the source products in Google Earth Engine.
Use the ELLIOT splits. The elliot.parquet file provides pre-built monotemporal and temporal splits designed for multi-sensor, multi-temporal EO research.

All files are self-contained GeoParquet with ZSTD compression, sorted by majortom:code_1000km → majortom:code_100km → id for efficient spatial predicate pushdown.

Schema

Columns are organized into namespaces. Each namespace groups related attributes.

Grid (`majortom:`)

Tile identity and spatial reference within the Major TOM grid system.

Column	Type	Description
`id`	string	Unique tile identifier (e.g. `MT10_770U_395R`).
`majortom:code_100km`	string	Parent 100 km grid cell. Used for spatial grouping.
`majortom:code_1000km`	string	Parent 1000 km grid cell. Used for coarse-level partitioning.
`majortom:crs`	string	Native UTM CRS of the tile (e.g. `EPSG:32647`).
`majortom:mgrs_tile`	string	MGRS tile code (e.g. `47WNS`). Links to Sentinel-2 tiling grid.
`majortom:mgrs_n`	uint8	Number of overlapping MGRS tiles (1 after deduplication).
`majortom:mgrs_candidates`	list<string>	All candidate MGRS tiles before deduplication.
`majortom:footprint_pct`	float	Percentage of tile covered by the assigned MGRS tile.
`majortom:geotransform`	list<int32>	Snapped affine geotransform [originX, scaleX, shearX, originY, shearY, scaleY].
`majortom:geotransform_raw`	list<double>	Original (unsnapped) affine geotransform.

STAC (`stac:`)

Spatial and temporal reference following STAC conventions. Present in land_s2 and land_l8 only, where it replaces the majortom: grid columns.

Column	Type	Description
`stac:crs`	string	Coordinate reference system.
`stac:geotransform`	list<int64>	Affine geotransform for the image patch.
`stac:tensor_shape`	list<int32>	Shape of the image tensor [bands, height, width].
`stac:time_start`	int64	Acquisition start time (Unix timestamp).
`stac:time_end`	int64	Acquisition end time (Unix timestamp).

Sentinel-2 (`s2:`)

Sensor metadata for the assigned Sentinel-2 image. Present in land_s2 only.

Column	Type	Description
`s2:id_gee`	string	Google Earth Engine image ID. Use this to fetch the actual imagery.
`s2:product_id`	string	ESA product identifier.
`s2:spacecraft`	string	Spacecraft name (Sentinel-2A or Sentinel-2B).
`s2:processing_baseline`	string	Processing baseline version.
`s2:orbit_number`	uint16	Relative orbit number.
`s2:mean_solar_azimuth`	float	Mean solar azimuth angle, averaged across all bands and detectors (degrees).
`s2:mean_solar_zenith`	float	Mean solar zenith angle, averaged across all bands and detectors (degrees).
`s2:mean_view_azimuth`	float	Mean viewing azimuth angle from band B8 (degrees).
`s2:mean_view_zenith`	float	Mean viewing zenith angle from band B8 (degrees).
`s2:reflectance_conversion`	float	Reflectance conversion factor (U correction).

Note on solar vs viewing angles. The sun has a single position relative to the scene, so ESA provides one solar azimuth and one solar zenith averaged across all bands. Viewing angles are different: Sentinel-2 uses a pushbroom sensor where each spectral band has its own detector array in the focal plane, each observing from a slightly different angle. That is why GEE provides per-band viewing angles (MEAN_INCIDENCE_*_ANGLE_B1 through _B12). We use band B8 (NIR, 10 m) as the reference because it is at native 10 m resolution and sits near the center of the focal plane, making it a representative proxy for the viewing geometry of the 10 m and 20 m bands.

Landsat 8/9 (`l8:`)

Sensor metadata for the assigned Landsat image. Present in land_l8 only.

Column	Type	Description
`l8:id_gee`	string	Google Earth Engine image ID. Use this to fetch the actual imagery.
`l8:product_id`	string	USGS product identifier.
`l8:spacecraft`	string	Spacecraft name (Landsat 8 or Landsat 9).
`l8:collection_number`	uint8	USGS Collection number.
`l8:collection_category`	string	Collection category (T1, T2, RT).
`l8:processing_software`	string	Processing software version.
`l8:wrs_path`	uint16	WRS-2 path number.
`l8:wrs_row`	uint16	WRS-2 row number.
`l8:cloud_cover`	float	Scene cloud cover percentage.
`l8:sun_azimuth`	float	Sun azimuth angle (degrees).
`l8:sun_elevation`	float	Sun elevation angle (degrees).
`l8:earth_sun_distance`	float	Earth-Sun distance (astronomical units).
`l8:image_quality_oli`	uint8	OLI image quality score.
`l8:roll_angle`	float	Spacecraft roll angle (degrees).

Terrain (`terrain:`)

Column	Type	Range	Description
`terrain:elevation`	float	~-420 to 8,849 (m)	Mean elevation in meters from the Copernicus GLO-30 DEM, a 30 m resolution Digital Surface Model derived from TanDEM-X radar satellite data (2011 to 2015). Includes buildings, infrastructure, and vegetation. Uses the EGM2008 vertical datum.

Climate (`climate:`)

Column	Type	Range	Description
`climate:precipitation`	float	0+ (mm/year)	Mean annual precipitation estimated from GPM (Global Precipitation Measurement) satellite data, aggregated as a long-term annual mean.
`climate:temperature`	float	~-40 to 50 (°C)	Mean annual land surface temperature estimated from MODIS LST satellite data, aggregated as a long-term annual mean.

Soil (`soil:`)

Surface-layer soil properties from the OpenLandMap dataset, derived from machine learning predictions on global soil survey data at 250 m resolution.

Column	Type	Range	Description
`soil:clay`	float	0 to 100 (%)	Clay content weight fraction at 0 cm depth. Source.
`soil:sand`	float	0 to 100 (%)	Sand content weight fraction at 0 cm depth. Source.
`soil:carbon`	float	0+ (g/kg)	Soil organic carbon content at 0 cm depth. Source.
`soil:bulk_density`	float	0+ (kg/m³)	Fine-earth bulk density at 0 cm depth. Source.
`soil:ph`	float	~3 to 10	Soil pH in water at 0 cm depth. Source.

Socioeconomic (`socio:`)

Column	Type	Range	Description
`socio:gdp`	float	0+ (USD)	GDP per capita at purchasing power parity (PPP, constant 2021 USD) for the year 2022. From the Kummu et al. (2025) gridded dataset, downscaled to admin-2 level (43,501 units) at 5 arc-min resolution. GEE catalog.
`socio:population`	float	0+ (people)	Estimated number of people per grid cell from the Meta High Resolution Settlement Layer (HRSL). Uses satellite imagery and census data at ~30 m resolution.
`socio:human_modification`	float	0.0 to 1.0	Cumulative degree of human modification of terrestrial ecosystems from the Global Human Modification v3 (Theobald et al. 2025). Combines the spatial footprint and intensity of 13 stressors across five categories: settlement, agriculture, transportation, mining/energy, and electrical infrastructure. 0 = no modification, 1 = fully modified. 300 m resolution. GEE catalog.
`socio:cisi`	float	0.0 to 1.0	Critical Infrastructure Spatial Index (Nirandjan et al. 2022). Aggregates OpenStreetMap data on 39 types of critical infrastructure across seven systems: transportation, energy, telecommunication, waste, water, education, and health. 0 = no infrastructure, 1 = highest density. 0.10° resolution. GEE catalog.

Administrative (`admin:`)

Human-readable administrative boundary names resolved from rasterized boundary datasets.

Column	Type	Description
`admin:country`	string	Country name. Tiles over ocean/lakes are labeled `Ocean/Sea/Lakes`.
`admin:state`	string	State or province name.
`admin:district`	string	District or county name.

Other

Column	Type	Description
`geometry`	binary (WKB)	Tile geometry. All files include GeoParquet metadata for spatial queries.
`split`	string	ELLIOT split assignment: `monotemporal` or `temporal`. Present in `elliot.parquet` only.

Files

File	Rows	Columns	Size	Description
`global.parquet`	5,055,204	26	146 MB	Every 10 km tile on Earth. The complete grid with all enrichment columns.
`land.parquet`	2,767,104	26	91 MB	Tiles covered by land-observing sensors (Sentinel-2 and Landsat). Same schema as global.
`land_s2.parquet`	2,547,253	34	127 MB	Land tiles with a Sentinel-2 image assigned. Adds `stac:` and `s2:` sensor metadata.
`land_l8.parquet`	2,255,537	38	97 MB	Land tiles with a Landsat 8/9 image assigned. Adds `stac:` and `l8:` sensor metadata.
`elliot.parquet`	279,166	27	14 MB	ELLIOT subset with monotemporal and temporal split assignments. Same enrichment as global plus `split` column.

Namespace availability per file

Namespace	global	land	land_s2	land_l8	elliot
`majortom:`	✓	✓			✓
`stac:`			✓	✓
`s2:`			✓
`l8:`				✓
`terrain:`	✓	✓	✓	✓	✓
`climate:`	✓	✓	✓	✓	✓
`soil:`	✓	✓	✓	✓	✓
`socio:`	✓	✓	✓	✓	✓
`admin:`	✓	✓	✓	✓	✓
`split`					✓
`geometry`	✓	✓	✓	✓	✓

Quick Start

DuckDB

Pandas

ELLIOT Splits

The split column defines two subsets:

Monotemporal (250,000 tiles). One cloud-free image per sensor per location. Designed for tasks where spatial coverage matters more than temporal depth: land cover classification, feature extraction, or pretraining foundation models on diverse global scenes.
Temporal (29,166 tiles). Multiple observations per location across time. Designed for tasks that require temporal context: change detection, phenology tracking, seasonal compositing, or training models that learn from multi-temporal sequences. This subset is further divided into monthly cadence (12,500 tiles × 12 timesteps) and five-daily cadence (16,666 tiles × 6 timesteps).

Citation

License

CC-BY-4.0

Index

Schema

Grid (majortom:)

STAC (stac:)

Sentinel-2 (s2:)

Landsat 8/9 (l8:)

Terrain (terrain:)

Climate (climate:)

Soil (soil:)

Socioeconomic (socio:)

Administrative (admin:)

Other

Files

Namespace availability per file

Quick Start

DuckDB

Pandas

ELLIOT Splits

Citation

License

Acknowledgements

Index

Index

Schema

Grid (majortom:)

STAC (stac:)

Sentinel-2 (s2:)

Landsat 8/9 (l8:)

Terrain (terrain:)

Climate (climate:)

Soil (soil:)

Socioeconomic (socio:)

Administrative (admin:)

Other

Files

Namespace availability per file

Quick Start

DuckDB

Pandas

ELLIOT Splits

Citation

License

Acknowledgements

Index

Schema

Grid (majortom:)

STAC (stac:)

Sentinel-2 (s2:)

Landsat 8/9 (l8:)

Terrain (terrain:)

Climate (climate:)

Soil (soil:)

Socioeconomic (socio:)

Administrative (admin:)

Other

Files

Namespace availability per file

Quick Start

DuckDB

Pandas

ELLIOT Splits

Citation

License

Acknowledgements

Index

Schema

Grid (majortom:)

STAC (stac:)

Sentinel-2 (s2:)

Landsat 8/9 (l8:)

Terrain (terrain:)

Climate (climate:)

Soil (soil:)

Socioeconomic (socio:)

Administrative (admin:)

Other

Files

Namespace availability per file

Quick Start

DuckDB

Grid (`majortom:`)

STAC (`stac:`)

Sentinel-2 (`s2:`)

Landsat 8/9 (`l8:`)

Terrain (`terrain:`)

Climate (`climate:`)

Soil (`soil:`)

Socioeconomic (`socio:`)

Administrative (`admin:`)

Grid (`majortom:`)

STAC (`stac:`)

Sentinel-2 (`s2:`)

Landsat 8/9 (`l8:`)

Terrain (`terrain:`)

Climate (`climate:`)

Soil (`soil:`)

Socioeconomic (`socio:`)

Administrative (`admin:`)

Grid (`majortom:`)

STAC (`stac:`)

Sentinel-2 (`s2:`)

Landsat 8/9 (`l8:`)

Terrain (`terrain:`)

Climate (`climate:`)

Soil (`soil:`)

Socioeconomic (`socio:`)

Administrative (`admin:`)

Grid (`majortom:`)

STAC (`stac:`)

Sentinel-2 (`s2:`)

Landsat 8/9 (`l8:`)

Terrain (`terrain:`)

Climate (`climate:`)

Soil (`soil:`)

Socioeconomic (`socio:`)

Administrative (`admin:`)