CoordBench

A unified benchmark suite for evaluating location encoders such as SatCLIP, GeoCLIP, Climplicit, and MIND. The dataset contains 40 normalized source tables from 13 source families. The paper's evaluation suite uses 52 datasets and 78 prediction targets drawn from this mirror. The source files previously lived across GitHub, figshare, GCS, Socrata, Zenodo, and Google Drive.

Intended use

Use the normalized tables to compare coordinate-to-embedding models. The HF config is defined by the original source table, not by individual targets. The paper evaluates 52 datasets and 78 targets with random five-fold cross-validation and latitude--longitude regional holdouts.

The normalization pipeline preserves source columns where possible, but it also joins coordinate columns, creates procedural samples for polygon and raster sources, and applies source-specific parsing. Read each config's provenance notes for more information.

The benchmark does not provide a universal license. Please refer to the per-config license table and the original source terms before redistribution or commercial use.

Load one table

import pyarrow.parquet as pq
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    "XXXX/CoordBench",
    "data/california_housing/data.parquet",
    repo_type="dataset",
    token=False,
)
table = pq.read_table(path, columns=["lon", "lat", "MedHouseVal"])
print(table.num_rows, table.column_names)

The remaining configs use the same data/<config>/data.parquet layout.

Schema

Every config's data.parquet carries these columns up front:

column	type	notes
lon, lat	float64	WGS84, always non-null
timestamp	int64 (nullable)	unix ms, UTC; null when the source has no per-point time
timestamp_end	int64 (nullable)	end of the valid-time window, when the source defines one (only dm_*)
split	string (nullable)	an official train/val/test partition from the source, null when it doesn't exist
id	string (nullable)	source row identifier, when one exists

...followed by the source columns retained by the normalization pipeline. Nodata values (-999, -1, ocean index 0) and untransformed values (no log1p) are preserved where the source table is copied. The config notes identify joins, procedural samples, and source-specific parsing.

Note there's one config per original source file/table, not per label task e.g. sustainbench's 6 DHS indices and cdc_places's 12 health measures are each one config since they're one shared source table.

A raw/ folder alongside data/ contains the original unmodified copies of the original datasets files.

License

No single license applies to the whole repo. Most configs are CC-BY-4.0/CC-BY-SA-4.0/public domain however some are unique -- see the status column below:

config	task	license	source
air_temp	regression	figshare-hosted (Hooker et al. 2018); not explicitly confirmed	https://api.figshare.com/v2/file/download/12609182
bt_bioclim	regression	CC-BY-SA-4.0 (data); MIT (code)	https://github.com/vdplasthijs/better_together
bt_biomass	regression	CC-BY-SA-4.0 (data); MIT (code)	https://github.com/vdplasthijs/better_together
bt_cropharvest	classification	CC-BY-SA-4.0 (data); MIT (code)	https://github.com/vdplasthijs/better_together
bt_human_footprint	regression	CC-BY-SA-4.0 (data); MIT (code)	https://github.com/vdplasthijs/better_together
bt_landcover	regression	CC-BY-SA-4.0 (data); MIT (code)	https://github.com/vdplasthijs/better_together
california_housing	regression	public domain (StatLib)	sklearn.datasets.fetch_california_housing (StatLib, Pace & Barry 1997)
cdc_places	regression	US federal government work / CDC Open Data (Socrata) - public	https://data.cdc.gov/resource/c7b2-4ecy.csv?$limit=60000
dm_africa_crop_mask	classification	CC-BY-4.0	https://zenodo.org/records/16585402
dm_aster_ged	regression	CC-BY-4.0	https://zenodo.org/records/16585402
dm_canada_crops_coarse	classification	Open Government Licence - Canada	https://zenodo.org/records/16585402
dm_canada_crops_fine	classification	Open Government Licence - Canada	https://zenodo.org/records/16585402
dm_descals	classification	CC-BY-4.0	https://zenodo.org/records/16585402
dm_ethiopia_crops	classification	CC-BY-4.0	https://zenodo.org/records/16585402
dm_glance	classification	CC-BY-4.0	https://zenodo.org/records/16585402
dm_lcmap_lc	classification	CC-BY-4.0	https://zenodo.org/records/16585402
dm_lcmap_lcc	classification	CC-BY-4.0	https://zenodo.org/records/16585402
dm_lcmap_lu	classification	CC-BY-4.0	https://zenodo.org/records/16585402
dm_lcmap_luc	classification	CC-BY-4.0	https://zenodo.org/records/16585402
dm_lucas_lc	classification	CC-BY-4.0	https://zenodo.org/records/16585402
dm_lucas_lu	classification	CC-BY-4.0	https://zenodo.org/records/16585402
dm_openet_ensemble	regression	CC-BY-4.0	https://zenodo.org/records/16585402
dm_us_trees	classification	CC-BY-NC-4.0 (iNaturalist)	https://zenodo.org/records/16585402
ecoregions	classification	CC-BY-4.0	https://storage.googleapis.com/teow2016/Ecoregions2017.zip
country	classification	public domain	https://raw.githubusercontent.com/nvkelso/natural-earth-vector/master/geojson/ne_110m_admin_0_countries.geojson
pdfm_conus27	regression	Apache-2.0 (repo); data file itself not explicitly cleared	https://raw.githubusercontent.com/google-research/population-dynamics/master/data/benchmarks/conus27.csv
satclip_country	classification	unspecified (Google Drive share, github.com/microsoft/satclip issue #6)	https://drive.google.com/drive/folders/1tI2qo6iioRrv3P1OxSXwHKObpLCrinad
satclip_ecoregion	classification	unspecified (Google Drive share, github.com/microsoft/satclip issue #6)	https://drive.google.com/drive/folders/1tI2qo6iioRrv3P1OxSXwHKObpLCrinad
satclip_elevation	regression	unspecified (Google Drive share, github.com/microsoft/satclip issue #6)	https://drive.google.com/drive/folders/1tI2qo6iioRrv3P1OxSXwHKObpLCrinad
satclip_population	regression	unspecified (Google Drive share, github.com/microsoft/satclip issue #6)	https://drive.google.com/drive/folders/1tI2qo6iioRrv3P1OxSXwHKObpLCrinad
soilgrids	regression	CC-BY-4.0	https://files.isric.org/soilgrids/latest/data/{prop}/{prop}_0-5cm_mean.vrt
sustainbench	regression	DHS Program-derived indices; redistribution terms not confirmed (dhsprogram.com access-gated)	https://api.figshare.com/v2/articles/26026798 (TorchSpatial/LocBench pack)
usavars_elevation	regression	CC-BY-4.0	https://hf.co/datasets/torchgeo/usavars/resolve/01377abfaf50c0cc8548aaafb79533666bbf288f/elevation.csv
usavars_housing	regression	CC-BY-4.0	https://hf.co/datasets/torchgeo/usavars/resolve/01377abfaf50c0cc8548aaafb79533666bbf288f/housing.csv
usavars_income	regression	CC-BY-4.0	https://hf.co/datasets/torchgeo/usavars/resolve/01377abfaf50c0cc8548aaafb79533666bbf288f/income.csv
usavars_nightlights	regression	CC-BY-4.0	https://hf.co/datasets/torchgeo/usavars/resolve/01377abfaf50c0cc8548aaafb79533666bbf288f/nightlights.csv
usavars_population	regression	CC-BY-4.0	https://hf.co/datasets/torchgeo/usavars/resolve/01377abfaf50c0cc8548aaafb79533666bbf288f/population.csv
usavars_roads	regression	CC-BY-4.0	https://hf.co/datasets/torchgeo/usavars/resolve/01377abfaf50c0cc8548aaafb79533666bbf288f/roads.csv
usavars_treecover	regression	CC-BY-4.0	https://hf.co/datasets/torchgeo/usavars/resolve/01377abfaf50c0cc8548aaafb79533666bbf288f/treecover.csv
worldclim_bio	regression	non-commercial/academic use only; redistribution requires WorldClim's permission	https://geodata.ucdavis.edu/climate/worldclim/2_1/base/wc2.1_10m_bio.zip

Comparison with MIND

Note that the bt-* and country and ecoregions datasets were added at a later date to CoordBench so to reproduce MIND results from the paper exclude these from your evals.