FTW-Planet

Paired PlanetScope SR scenes (two windows per AOI — early- and peak-season) co-registered with Fields of The World v2 field-boundary labels, across 25 countries.

• 66,584 patches, 25 countries
• 52,235 patches with both windows passing UDM2 usability (usable_pair = True)
• Imagery: PlanetScope ortho_analytic_4b_sr, 4 bands (B/G/R/NIR), 3 m GSD, native UTM, uint16 (reflectance = DN / 10000)
• Labels: 3 classes — 0 background, 1 field interior, 2 field boundary; uint8 with NBITS=2; boundaries rasterized with all_touched=True to match the FTW originals.

Layout

taylor-geospatial/ftw-planet/
├── README.md
├── index.parquet           # GeoParquet 1.1, one row per patch
└── dataset/
    ├── austria.tar
    ├── ...
    └── vietnam.tar         # 25 country shards, ~96 GiB total

Each tar is a WebDataset shard with five files per patch_id:

<pid>.window_a.tif        PlanetScope SR, window A
<pid>.window_b.tif        PlanetScope SR, window B
<pid>.label.tif           3-class label
<pid>.polygons.parquet    true FTW field polygons, clipped to the patch
<pid>.json                metadata (mirrors the index row)

<pid>.polygons.parquet holds the original FTW vector field boundaries reprojected to the patch's UTM grid and clipped to its bounds — the same vector source the .label.tif raster is burned from, so you can score polygon-level metrics against true geometry rather than connected components of the mask. Columns: id, geometry, area_ha (true planimetric area in hectares), plus any of crop_id / crop_name / area / perimeter present in the source. Patches with no fields carry an empty (0-row) GeoParquet, so every sample has the file.

Tars are uncompressed; the TIFFs inside are ZSTD-22. They stream as WebDataset shards and also extract cleanly with tar -xf <country>.tar.

Downloading

from huggingface_hub import hf_hub_download, snapshot_download

# one country shard
path = hf_hub_download("taylor-geospatial/ftw-planet", "dataset/rwanda.tar", repo_type="dataset")

# the whole dataset
snapshot_download("taylor-geospatial/ftw-planet", repo_type="dataset", local_dir="ftw-planet")

Reading the index

import geopandas as gpd
from huggingface_hub import hf_hub_download

idx = hf_hub_download("taylor-geospatial/ftw-planet", "index.parquet", repo_type="dataset")
gdf = gpd.read_parquet(idx)
clean = gdf[gdf.usable_pair & (gdf.cloud_cover_a < 0.05) & (gdf.cloud_cover_b < 0.05)]

The index is GeoParquet 1.1 with a bbox covering struct and is Hilbert-sorted into 14 row groups, so spatial queries from DuckDB / duckdb-wasm can prune row groups by bbox without parsing WKB:

INSTALL spatial; LOAD spatial; INSTALL httpfs; LOAD httpfs;

SELECT patch_id, country
FROM 'index.parquet'
WHERE bbox.xmin > -10 AND bbox.xmax < 25
  AND bbox.ymin > 35  AND bbox.ymax < 60
  AND usable_pair;

Index columns

Identity / geometry:

column	type	notes
patch_id	str	unique within country
country	str	one of 25 slugs
geometry	polygon	EPSG:4326 patch footprint
crs	str	native UTM CRS of the tifs (e.g. EPSG:32636)
bounds_4326	float[4]	[minx, miny, maxx, maxy] convenience field

Paths (relative to the tar / planet root):

column	example
image_a_path	rwanda/window_a/1592589.tif
image_b_path	rwanda/window_b/1592589.tif
label_path	rwanda/labels/1592589.tif

Scene provenance, per window suffix _a / _b:

column	notes
item_id_{a,b}	PlanetScope item ID
scene_date_{a,b}	UTC acquisition timestamp
cloud_cover_{a,b}	scene-level fraction in [0,1]
coverage_{a,b}	AOI coverage of the source scene
source_{a,b}	source product / pipeline tag

Per-patch UDM2 statistics (fraction of pixels in the patch), per window:

column	meaning
udm2_clear_{a,b}	clear sky
udm2_cloud_{a,b}	cloud
udm2_shadow_{a,b}	cloud shadow
udm2_light_haze_{a,b}	light haze
udm2_heavy_haze_{a,b}	heavy haze
udm2_snow_{a,b}	snow / ice
udm2_unusable_{a,b}	UDM2 unusable mask
udm2_confidence_mean_{a,b}	mean UDM2 confidence band
udm2_usable_flag_{a,b}	bool — derived per-patch quality

FTW season metadata:

column	notes
ftw_target_date_{a,b}	target acquisition date for each window
ftw_season_start	growing-season start (per FTW)
ftw_season_end	growing-season end (per FTW)

Quality:

column	type	notes
usable_pair	bool	both windows pass UDM2 usability — the primary training subset

Licensing

This dataset is released under CC-BY-NC-4.0 (non-commercial).

Imagery is © Planet Labs PBC. The included AOIs were exported under the NICFI / research program — refer to those terms for redistribution. FTW v2 field-boundary polygons are CC-BY-4.0; see fieldsoftheworld/ftw-baselines for source terms.