MSG (Meteosat Second Generation) imagery subset from the Global 3D Cloud Reconstruction Dataset. Contains multispectral geostationary satellite imagery from MSG/SEVIRI for 3D cloud structure reconstruction. Each sample contains 12 spectral channels. 512x512 pixel patches in Cloud-Optimized GeoTIFF format.
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Hierarchical structure showing representative samples across levels. The "..." notation indicates additional samples following the same pattern. All samples at the same level share identical structure (RSUT constraint).
| Level | Types | Total Samples | Sample IDs (preview) |
|---|---|---|---|
| Level 0 | All FILE |
50,000 | Root level samples |
These fields are available for querying with SQL when using TacoReader.
| Field Name | Type | Description |
|---|---|---|
| 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 |
| stac:crs | string | Coordinate reference system (WKT2, EPSG, or PROJ string) |
| stac:tensor_shape | list<item: int64> | Raster dimensions e.g. [bands, height, width] |
| stac:geotransform | list<item: double> | GDAL affine transform [origin_x, pixel_width, rot_x, origin_y, rot_y, pixel_height] |
| stac:time_start | timestamp[us] | Acquisition start timestamp (microseconds since Unix epoch, UTC) |
| stac:centroid | binary | Raster center point in EPSG:4326 (WKB binary format) |
| stac:time_end | timestamp[us] | Acquisition end timestamp (microseconds since Unix epoch, UTC) |
| stac:time_middle | timestamp[us] | Midpoint between start and end timestamps (microseconds since Unix epoch, UTC) |
| 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] |
| cloud3d:satellite | string | Geostationary satellite platform (GOES, HIMAWARI, or MSG) |
| 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:precipitation | float | Mean annual precipitation in mm estimated from GPM data |
| geoenrich:temperature | float | Mean annual temperature in °C estimated from MODIS LST data |
| geoenrich:admin_countries | string | Country 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). |
# pip install tacoreader
import tacoreader
# Load dataset
ds = tacoreader.load("https://data.source.coop/taco/3dclouds/pretraining/msg/")
# Basic info
print(f"ID: {ds.id}")
print(f"Version: {ds.version}")
print(f"Samples: {len(ds.data)}")| Name | Organization | |
|---|---|---|
| Cesar Aybar | Universitat de València | cesar.aybar@uv.es |
| Shirin Ermis | University of Oxford | — |
| Lilli Freischem | University of Oxford | — |
| Stella Girtsou | National Observatory of Athens | — |
| Kyriaki-Margarita Bintsi | Harvard Medical School | — |
| Emiliano Diaz Salas-Porras | Universitat de València | — |
| Michael Eisinger | European Space Agency | — |
| William Jones | University of Oxford | — |
| Anna Jungbluth | European Space Agency | — |
| Benoit Tremblay | Environment and Climate Change Canada | — |
If you use this dataset in your research, please cite:
@dataset{cloud3d-pretraining-msg0,
title = {Cloud 3D - MSG Pretraining Dataset},
author = {Cesar Aybar and Shirin Ermis and Lilli Freischem and Stella Girtsou and Kyriaki-Margarita Bintsi and Emiliano Diaz Salas-Porras and Michael Eisinger and William Jones and Anna Jungbluth and Benoit Tremblay},
year = {2004},
version = {0.1.0},
publisher = {Universitat de València}
}