1import geopandas as gpd
2
3gpd.read_parquet("s3://us-west-2.opendata.source.coop/ftw/global-data/features/cogs/alpha/index.parquet")

1import geopandas as gpd
2
3gpd.read_parquet("s3://us-west-2.opendata.source.coop/ftw/global-data/features/cogs/alpha/index.parquet")

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

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

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

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

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└─────────────┴────────────────────────────────────────────────────────────┴─────────┴─────────┴─────────┴─────────┘

1@inproceedings{kerner2025fields,
2  title={Fields of the world: A machine learning benchmark dataset for global agricultural field boundary segmentation},
3  author={Kerner, Hannah and Chaudhari, Snehal and Ghosh, Aninda and Robinson, Caleb and Ahmad, Adeel and Choi, Eddie and Jacobs, Nathan and Holmes, Chris and Mohr, Matthias and Dodhia, Rahul and others},
4  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
5  volume={39},
6  number={27},
7  pages={28151--28159},
8  year={2025}
9}

1@inproceedings{kerner2025fields,
2  title={Fields of the world: A machine learning benchmark dataset for global agricultural field boundary segmentation},
3  author={Kerner, Hannah and Chaudhari, Snehal and Ghosh, Aninda and Robinson, Caleb and Ahmad, Adeel and Choi, Eddie and Jacobs, Nathan and Holmes, Chris and Mohr, Matthias and Dodhia, Rahul and others},
4  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
5  volume={39},
6  number={27},
7  pages={28151--28159},
8  year={2025}
9}

1@article{muhawenayo2026prue,
2  title={PRUE: A Practical Recipe for Field Boundary Segmentation at Scale},
3  author={Muhawenayo, Gedeon and Robinson, Caleb and Khanal, Subash and Fang, Zhanpei and Corley, Isaac and Wollam, Alexander and Gao, Tianyi and Strnad, Leonard and Avery, Ryan and Estes, Lyndon and others},
4  journal={arXiv preprint arXiv:2603.27101},
5  year={2026}
6}

1@article{muhawenayo2026prue,
2  title={PRUE: A Practical Recipe for Field Boundary Segmentation at Scale},
3  author={Muhawenayo, Gedeon and Robinson, Caleb and Khanal, Subash and Fang, Zhanpei and Corley, Isaac and Wollam, Alexander and Gao, Tianyi and Strnad, Leonard and Avery, Ryan and Estes, Lyndon and others},
4  journal={arXiv preprint arXiv:2603.27101},
5  year={2026}
6}

1@article{corley2026fields,
2  title={Fields of The World: A Field Guide for Extracting Agricultural Field Boundaries},
3  author={Corley, Isaac and Kerner, Hannah and Robinson, Caleb and Marcus, Jennifer},
4  journal={arXiv preprint arXiv:2602.08131},
5  year={2026}
6}

1@article{corley2026fields,
2  title={Fields of The World: A Field Guide for Extracting Agricultural Field Boundaries},
3  author={Corley, Isaac and Kerner, Hannah and Robinson, Caleb and Marcus, Jennifer},
4  journal={arXiv preprint arXiv:2602.08131},
5  year={2026}
6}