Major TOM — 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

INSTALL spatial;
LOAD spatial;

-- Count tiles per country in South America
SELECT "admin:country", COUNT(*) as n_tiles
FROM 'https://data.source.coop/majortom/index/land_s2.parquet'
WHERE "admin:country" IN ('Peru', 'Brazil', 'Colombia', 'Chile', 'Argentina')
GROUP BY "admin:country"
ORDER BY n_tiles DESC;

-- Find high-elevation, arid Sentinel-2 tiles
SELECT id, "s2:id_gee", "terrain:elevation", "climate:precipitation"
FROM 'https://data.source.coop/majortom/index/land_s2.parquet'
WHERE "terrain:elevation" > 3000
  AND "climate:precipitation" < 200
LIMIT 20;

Pandas

import pandas as pd

# Load land tiles with Sentinel-2 metadata
url = "https://data.source.coop/majortom/index/land_s2.parquet"
df = pd.read_parquet(url)

# Filter by country
peru = df[df["admin:country"] == "Peru"]
print(f"Peru: {len(peru):,} tiles")

# Get ELLIOT splits
elliot = pd.read_parquet(
    "https://data.source.coop/majortom/index/elliot.parquet"
)
print(elliot["split"].value_counts())

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).

License

This dataset is released under CC-BY-4.0.

Citation

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

Acknowledgments

This work was supported by the ELLIOT project, funded by the European Union under grant agreement No. 101214398. Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union.