Global Fields of The World (FTW)

Global Fields of The World (FTW) provides global-scale estimates of agricultural fields for 2024–2025. The dataset includes both model inputs (Sentinel-2–derived median composites in COG and Zarr v3 formats) and outputs (in Zarr, GeoParquet and PMTiles).

Product Details

Visibility: Unlisted
Owner: Fields of The World
Created: 4 Feb 2026
Last Updated: 13 Feb 2026

Product Contents

root

Product Details

Product Contents

Global Fields of The World (FTW)

Product Details

Visibility: Unlisted
Owner: Fields of The World
Created: 4 Feb 2026
Last Updated: 13 Feb 2026

Product Contents

root

10.94 KB

README

Global Fields of The World (FTW)

Taylor Geospatial Engine Labs presents mosaics and global predictions of agricultural fields for the years 2024 and 2025. These mosaics and field boundary predictions were built with Wherobots RasterFlow.

Data Products

features
- Version: alpha
- Planting and harvest features as median composites over 5-10 Sentinel-2 scenes.
- Saved out in COG and also reprojected to EPSG:4326 in a single Zarr V3 format as a single dataset.
predictions:
- Version: alpha
- Computed using features above as inputs to the PRUE Model on Hugging Face.
- Saved out in Zarr V3 format as a single dataset, GeoParquet v1.1.0 with covering, and PMTiles.

Features (COGs)

Location: s3://us-west-2.opendata.source.coop/ftw/global-data/features/cogs/alpha/.

Features are defined by selecting DOY ranges as planting/harvest heuristics and computing the median of masked pixels across ~5–10 scenes. See Appendix for additional information.

s2med_harvest/*:
- Contains bands ["B02", "B03", "B04", "B08", "N_VALID_PIXELS"]
- Each band is the median over the respective Sentinel-2 scenes.
- N_VALID_PIXELS is the number of valid scenes after quality-flag masking.
s2med_planting/*:
- Contains bands ["B02", "B03", "B04", "B08", "N_VALID_PIXELS"]
- Each band is the median over the respective Sentinel-2 scenes.
- N_VALID_PIXELS is the number of valid scenes after quality-flag masking.
index.parquet:

Features (Zarr)

Location: s3://data.source.coop/ftw/global-data/features/zarr/alpha/global.zarr

We reproject and resample all feature COGs to EPSG:4326 at 8.983119e-5° (~10 m at the equator) using GDAL cubic resampling, producing a single Zarr mosaic with dimensions (time, band, y, x).

Predictions (Zarr)

Location: s3://data.source.coop/ftw/global-data/predictions/zarr/alpha/global.zarr

We run the PRUE model over features/zarr/alpha/global.zarr to generate a stackable Zarr dataset with bands [non_field_background, field, field_boundaries]. Note that the feature and predictions zarr datasets share the same grid and are "stackable".

Features and Predictions

We can inspect inputs and outputs side-by-side since they are stackable. This enables researchers to validate inputs to better understand their influence in model outputs.

Predictions (GeoParquet)

Location: s3://us-west-2.opendata.source.coop/ftw/global-data/predictions/vectors/alpha/results/

A geoparquet vector dataset has been created from the prediction zarr dataset by simply thresholding the softmax outputs for the three labels [non_field_background, field, field_boundaries] by 0.5.

The files were created using the v1.1.0 spec. There are over 8217195679 rows across 1001 files totalling about 629Gb on S3 with the following schema:

Get started by using DuckDB and Lonboard for visualization.

Predictions (PMTiles)

Location: s3://us-west-2.opendata.source.coop/ftw/global-data/predictions/vectors/alpha/global.pmtiles

PMTiles are created from the vector data above enabling scalable visualization of the vectorized results.

More Info

See this blog post for more information on the platform used to generate these results. And see related data products such as fields of the world results for a few countries.

Appendix

Season Heuristics (`alpha`)

We determine relevant Sentinel-2 scenes by acquisition dates according to the following functions:

Sentinel-2 Source

All Sentinel-2 scenes were sourced from s3://sentinel-cogs/sentinel-s2-l2a-cogs.

Sentinel-2 SCL Flags

We use the following quality flags to mark Sentinel-2 bands as invalid as provided in the SCL band:

0: No Data
1: Saturated Defective
3: Cloud Shadow
7: Cloud Low Probability / Unclassified
8: Cloud Medium Probability
9: Cloud High Probability
10: Thin Cirrus

Contact

Email: len@wherobots.com

README

Global Fields of The World (FTW)

Data Products

features
- Version: alpha
- Planting and harvest features as median composites over 5-10 Sentinel-2 scenes.
- Saved out in COG and also reprojected to EPSG:4326 in a single Zarr V3 format as a single dataset.
predictions:
- Version: alpha
- Computed using features above as inputs to the PRUE Model on Hugging Face.
- Saved out in Zarr V3 format as a single dataset, GeoParquet v1.1.0 with covering, and PMTiles.

Features (COGs)

Location: s3://us-west-2.opendata.source.coop/ftw/global-data/features/cogs/alpha/.

Features are defined by selecting DOY ranges as planting/harvest heuristics and computing the median of masked pixels across ~5–10 scenes. See Appendix for additional information.

s2med_harvest/*:
- Contains bands ["B02", "B03", "B04", "B08", "N_VALID_PIXELS"]
- Each band is the median over the respective Sentinel-2 scenes.
- N_VALID_PIXELS is the number of valid scenes after quality-flag masking.
s2med_planting/*:
- Contains bands ["B02", "B03", "B04", "B08", "N_VALID_PIXELS"]
- Each band is the median over the respective Sentinel-2 scenes.
- N_VALID_PIXELS is the number of valid scenes after quality-flag masking.
index.parquet:

Features (Zarr)

Location: s3://data.source.coop/ftw/global-data/features/zarr/alpha/global.zarr

We reproject and resample all feature COGs to EPSG:4326 at 8.983119e-5° (~10 m at the equator) using GDAL cubic resampling, producing a single Zarr mosaic with dimensions (time, band, y, x).

Predictions (Zarr)

Location: s3://data.source.coop/ftw/global-data/predictions/zarr/alpha/global.zarr

Features and Predictions

We can inspect inputs and outputs side-by-side since they are stackable. This enables researchers to validate inputs to better understand their influence in model outputs.

Predictions (GeoParquet)

Location: s3://us-west-2.opendata.source.coop/ftw/global-data/predictions/vectors/alpha/results/

The files were created using the v1.1.0 spec. There are over 8217195679 rows across 1001 files totalling about 629Gb on S3 with the following schema:

Get started by using DuckDB and Lonboard for visualization.

Predictions (PMTiles)

Location: s3://us-west-2.opendata.source.coop/ftw/global-data/predictions/vectors/alpha/global.pmtiles

PMTiles are created from the vector data above enabling scalable visualization of the vectorized results.

More Info

See this blog post for more information on the platform used to generate these results. And see related data products such as fields of the world results for a few countries.

Appendix

Season Heuristics (`alpha`)

We determine relevant Sentinel-2 scenes by acquisition dates according to the following functions:

Sentinel-2 Source

All Sentinel-2 scenes were sourced from s3://sentinel-cogs/sentinel-s2-l2a-cogs.

Sentinel-2 SCL Flags

We use the following quality flags to mark Sentinel-2 bands as invalid as provided in the SCL band:

0: No Data
1: Saturated Defective
3: Cloud Shadow
7: Cloud Low Probability / Unclassified
8: Cloud Medium Probability
9: Cloud High Probability
10: Thin Cirrus

Contact

Email: len@wherobots.com

1┌─────────────┬────────────────────────────────────────────────────────────┬─────────┬─────────┬─────────┬─────────┐
2│ column_name │                        column_type                         │  null   │   key   │ default │  extra  │
3│   varchar   │                          varchar                           │ varchar │ varchar │ varchar │ varchar │
4├─────────────┼────────────────────────────────────────────────────────────┼─────────┼─────────┼─────────┼─────────┤
5│ geometry    │ GEOMETRY                                                   │ YES     │ NULL    │ NULL    │ NULL    │
6│ time        │ TIMESTAMP                                                  │ YES     │ NULL    │ NULL    │ NULL    │
7│ label       │ VARCHAR                                                    │ YES     │ NULL    │ NULL    │ NULL    │
8│ bbox        │ STRUCT(xmax DOUBLE, xmin DOUBLE, ymax DOUBLE, ymin DOUBLE) │ YES     │ NULL    │ NULL    │ NULL    │
9└─────────────┴────────────────────────────────────────────────────────────┴─────────┴─────────┴─────────┴─────────┘

1┌─────────────┬────────────────────────────────────────────────────────────┬─────────┬─────────┬─────────┬─────────┐
2│ column_name │                        column_type                         │  null   │   key   │ default │  extra  │
3│   varchar   │                          varchar                           │ varchar │ varchar │ varchar │ varchar │
4├─────────────┼────────────────────────────────────────────────────────────┼─────────┼─────────┼─────────┼─────────┤
5│ geometry    │ GEOMETRY                                                   │ YES     │ NULL    │ NULL    │ NULL    │
6│ time        │ TIMESTAMP                                                  │ YES     │ NULL    │ NULL    │ NULL    │
7│ label       │ VARCHAR                                                    │ YES     │ NULL    │ NULL    │ NULL    │
8│ bbox        │ STRUCT(xmax DOUBLE, xmin DOUBLE, ymax DOUBLE, ymin DOUBLE) │ YES     │ NULL    │ NULL    │ NULL    │
9└─────────────┴────────────────────────────────────────────────────────────┴─────────┴─────────┴─────────┴─────────┘

1import rasterix
2import xarray as xr
3
4features = xr.open_zarr(
5    "s3://data.source.coop/ftw/global-data/features/zarr/alpha/global.zarr"
6).pipe(rasterix.assign_index)
7features

1import rasterix
2import xarray as xr
3
4features = xr.open_zarr(
5    "s3://data.source.coop/ftw/global-data/features/zarr/alpha/global.zarr"
6).pipe(rasterix.assign_index)
7features

1features = xr.open_zarr(
2    "s3://data.source.coop/ftw/global-data/predictions/zarr/alpha/global.zarr"
3).pipe(rasterix.assign_index)
4features

1features = xr.open_zarr(
2    "s3://data.source.coop/ftw/global-data/predictions/zarr/alpha/global.zarr"
3).pipe(rasterix.assign_index)
4features

1planting_bands = ["s2med_planting:B04", "s2med_planting:B03", "s2med_planting:B02"]
2harvest_bands = ["s2med_harvest:B04", "s2med_harvest:B03", "s2med_harvest:B02"]
3xmin, ymin, xmax, ymax = -93.75, 41.5, -93.5, 41.75
4year = "2024"
5
6fig, axs = plt.subplots(1, 3, figsize=(15, 5))
7
8features["variables"].sel(
9    band=planting_bands, time=year, y=slice(ymax, ymin), x=slice(xmin, xmax)
10)[0].plot.imshow(robust=True, ax=axs[0])
11axs[0].axis("off")
12axs[0].set_title("Planting")
13
14features["variables"].sel(
15    band=harvest_bands, time=year, y=slice(ymax, ymin), x=slice(xmin, xmax)
16)[0].plot.imshow(robust=True, ax=axs[1])
17axs[1].axis("off")
18axs[1].set_title("Harvest")
19
20predictions["variables"].sel(band="field", time=year, y=slice(ymax, ymin), x=slice(xmin, xmax))[
21    0
22].plot.imshow(ax=axs[2])
23axs[2].axis("off")
24axs[2].set_title("Predictions")
25
26plt.tight_layout()
27plt.show()

1planting_bands = ["s2med_planting:B04", "s2med_planting:B03", "s2med_planting:B02"]
2harvest_bands = ["s2med_harvest:B04", "s2med_harvest:B03", "s2med_harvest:B02"]
3xmin, ymin, xmax, ymax = -93.75, 41.5, -93.5, 41.75
4year = "2024"
5
6fig, axs = plt.subplots(1, 3, figsize=(15, 5))
7
8features["variables"].sel(
9    band=planting_bands, time=year, y=slice(ymax, ymin), x=slice(xmin, xmax)
10)[0].plot.imshow(robust=True, ax=axs[0])
11axs[0].axis("off")
12axs[0].set_title("Planting")
13
14features["variables"].sel(
15    band=harvest_bands, time=year, y=slice(ymax, ymin), x=slice(xmin, xmax)
16)[0].plot.imshow(robust=True, ax=axs[1])
17axs[1].axis("off")
18axs[1].set_title("Harvest")
19
20predictions["variables"].sel(band="field", time=year, y=slice(ymax, ymin), x=slice(xmin, xmax))[
21    0
22].plot.imshow(ax=axs[2])
23axs[2].axis("off")
24axs[2].set_title("Predictions")
25
26plt.tight_layout()
27plt.show()

1import duckdb
2from lonboard import Map, PolygonLayer
3from lonboard.basemap import CartoStyle
4
5con = duckdb.connect()
6con.execute("INSTALL spatial; LOAD spatial;")
7con.execute("INSTALL httpfs; LOAD httpfs;")
8
9q = """
10SELECT geometry AS geometry, time, label, bbox
11FROM read_parquet('s3://us-west-2.opendata.source.coop/ftw/global-data/predictions/vectors/alpha/results/*.parquet')
12WHERE label = 'field'
13  AND struct_extract(bbox, 'xmax') >= -93.71488
14  AND struct_extract(bbox, 'xmin') <= -93.06492
15  AND struct_extract(bbox, 'ymax') >= 41.78201
16  AND struct_extract(bbox, 'ymin') <= 42.09459
17"""
18
19layer = PolygonLayer.from_duckdb(q, con, get_fill_color=[0, 0, 139])
20m = Map(layer, height=700, basemap_style=CartoStyle.Positron)
21m

1import duckdb
2from lonboard import Map, PolygonLayer
3from lonboard.basemap import CartoStyle
4
5con = duckdb.connect()
6con.execute("INSTALL spatial; LOAD spatial;")
7con.execute("INSTALL httpfs; LOAD httpfs;")
8
9q = """
10SELECT geometry AS geometry, time, label, bbox
11FROM read_parquet('s3://us-west-2.opendata.source.coop/ftw/global-data/predictions/vectors/alpha/results/*.parquet')
12WHERE label = 'field'
13  AND struct_extract(bbox, 'xmax') >= -93.71488
14  AND struct_extract(bbox, 'xmin') <= -93.06492
15  AND struct_extract(bbox, 'ymax') >= 41.78201
16  AND struct_extract(bbox, 'ymin') <= 42.09459
17"""
18
19layer = PolygonLayer.from_duckdb(q, con, get_fill_color=[0, 0, 139])
20m = Map(layer, height=700, basemap_style=CartoStyle.Positron)
21m

1import leafmap.foliumap as leafmap
2
3url = "s3://us-west-2.opendata.source.coop/ftw/global-data/predictions/vectors/alpha/global.pmtiles"
4
5metadata = leafmap.pmtiles_metadata(url)
6m = leafmap.Map(center=[0, 20], zoom=2, height="800px")
7m.add_basemap("SATELLITE", visible=True)
8style = leafmap.pmtiles_style(url, layers="field-2024-01-01 00:00:00", opacity=0.7)
9m.add_pmtiles(url, style=style)
10m

1import leafmap.foliumap as leafmap
2
3url = "s3://us-west-2.opendata.source.coop/ftw/global-data/predictions/vectors/alpha/global.pmtiles"
4
5metadata = leafmap.pmtiles_metadata(url)
6m = leafmap.Map(center=[0, 20], zoom=2, height="800px")
7m.add_basemap("SATELLITE", visible=True)
8style = leafmap.pmtiles_style(url, layers="field-2024-01-01 00:00:00", opacity=0.7)
9m.add_pmtiles(url, style=style)
10m

1def tile_to_planting_season_heuristic_doy(point: Point) -> tuple[int, int]:
2    lat = point.y
3    abs_lat = abs(lat)
4    if abs_lat > 45:
5        start_doy, end_doy = (91, 151) if lat > 0 else (274, 334)  # Apr-May or Oct-Nov
6    elif 20 < abs_lat <= 45:
7        start_doy, end_doy = (60, 120) if lat > 0 else (244, 334)  # Mar-Apr or Sep-Nov
8    elif 5 < abs_lat <= 20:
9        start_doy, end_doy = (121, 212) if lat > 0 else (305, 365)  # May-Jul or Nov-Dec
10    else:  # Equatorial
11        start_doy, end_doy = (60, 121)  # Approx. Mar-Apr and Sep-Oct (simplified)
12    return start_doy, end_doy
13
14
15def tile_to_harvest_season_heuristic_doy(point: Point) -> tuple[int, int]:
16    lat = point.y
17    abs_lat = abs(lat)
18    if abs_lat > 45:
19        start_doy, end_doy = (244, 304) if lat > 0 else (60, 151)  # Sep-Oct or Mar-May
20    elif 20 < abs_lat <= 45:
21        start_doy, end_doy = (213, 304) if lat > 0 else (32, 120)  # Aug-Oct or Feb-Apr
22    elif 5 < abs_lat <= 20:
23        start_doy, end_doy = (274, 365) if lat > 0 else (91, 181)  # Oct-Dec or Apr-Jun
24    else:  # Equatorial
25        start_doy, end_doy = (182, 243)  # Approx. Jul-Aug and Jan-Feb (simplified)
26    return start_doy, end_doy

1def tile_to_planting_season_heuristic_doy(point: Point) -> tuple[int, int]:
2    lat = point.y
3    abs_lat = abs(lat)
4    if abs_lat > 45:
5        start_doy, end_doy = (91, 151) if lat > 0 else (274, 334)  # Apr-May or Oct-Nov
6    elif 20 < abs_lat <= 45:
7        start_doy, end_doy = (60, 120) if lat > 0 else (244, 334)  # Mar-Apr or Sep-Nov
8    elif 5 < abs_lat <= 20:
9        start_doy, end_doy = (121, 212) if lat > 0 else (305, 365)  # May-Jul or Nov-Dec
10    else:  # Equatorial
11        start_doy, end_doy = (60, 121)  # Approx. Mar-Apr and Sep-Oct (simplified)
12    return start_doy, end_doy
13
14
15def tile_to_harvest_season_heuristic_doy(point: Point) -> tuple[int, int]:
16    lat = point.y
17    abs_lat = abs(lat)
18    if abs_lat > 45:
19        start_doy, end_doy = (244, 304) if lat > 0 else (60, 151)  # Sep-Oct or Mar-May
20    elif 20 < abs_lat <= 45:
21        start_doy, end_doy = (213, 304) if lat > 0 else (32, 120)  # Aug-Oct or Feb-Apr
22    elif 5 < abs_lat <= 20:
23        start_doy, end_doy = (274, 365) if lat > 0 else (91, 181)  # Oct-Dec or Apr-Jun
24    else:  # Equatorial
25        start_doy, end_doy = (182, 243)  # Approx. Jul-Aug and Jan-Feb (simplified)
26    return start_doy, end_doy

Global Fields of The World (FTW)

Global Fields of The World (FTW)

Global Fields of The World (FTW)

Data Products

Features (COGs)

Features (Zarr)

Predictions (Zarr)

Features and Predictions

Predictions (GeoParquet)

Predictions (PMTiles)

More Info

Appendix

Season Heuristics (alpha)

Sentinel-2 Source

Sentinel-2 SCL Flags

Contact

Global Fields of The World (FTW)

Data Products

Features (COGs)

Features (Zarr)

Predictions (Zarr)

Features and Predictions

Predictions (GeoParquet)

Predictions (PMTiles)

More Info

Appendix

Season Heuristics (alpha)

Sentinel-2 Source

Sentinel-2 SCL Flags

Contact

Season Heuristics (`alpha`)

Season Heuristics (`alpha`)