Datasets:

taylor-geospatial
/

coordbench

This dataset was designed to provide a framework and set of evaluations for assessing the performance of geospatial models and datasets on real-world, data-limited applications. It is comprised of fifteen individual evaluations derived from ten publicly-available data sources. These datasets were selected and processed to represent archetypal classification, regression, and change detection use cases. A list of short names used to refer to individual evaluation datasets, names of the sources these datasets were derived from, and path to the location within this archive are provided in the table below. The following sections provide a basic overview of general pre-processing, and a README file is provided for each individual data source and dataset.

Short name	Source name	Path
`africa_crop_mask`	Comparison of Cropland Maps Derived from Land Cover Maps in Sub-Saharan Africa	`./africa_crop_mask`
`aster_ged`	AG100: ASTER Global Emissivity Dataset 100-meter V003	`./aster_ged`
`canada_crops_coarse`	Agriculture and Agri-Food Canada (AAFC) Annual Crop Inventory Ground Truth Data	`./canada_crops/canada_crops_coarse`
`canada_crops_fine`	Agriculture and Agri-Food Canada (AAFC) Annual Crop Inventory Ground Truth Data	`./canada_crops/canada_crops_fine`
`descals`	Global oil palm extent and planting year from 1990 to 2021	`./descals`
`ethiopia_crops`	Ethiopian Crop Type 2020 (EthCT2020) dataset	`./ethiopia_crops`
`glance`	GLanCE: A Global Land Cover Training Dataset from 1984 to 2020	`./glance`
`lcmap_lc`	LCMAP CONUS Reference Data Product 1984-2021 land cover, land use and change process attributes	`./lcmap/lcmap_lc`
`lcmap_lu`	LCMAP CONUS Reference Data Product 1984-2021 land cover, land use and change process attributes	`./lcmap/lcmap_lu`
`lcmap_lcc`	LCMAP CONUS Reference Data Product 1984-2021 land cover, land use and change process attributes	`./lcmap/lcmap_lcc`
`lcmap_luc`	LCMAP CONUS Reference Data Product 1984-2021 land cover, land use and change process attributes	`./lcmap/lcmap_luc`
`lucas_lc`	LUCAS: Land Use and Coverage Area frame Survey	`./lucas/lucas_lc`
`lucas_lu`	LUCAS: Land Use and Coverage Area frame Survey	`./lucas/lucas_lu`
`openet_ensemble`	OpenET Ensemble Monthly Evapotranspiration v2.0	`./openet_ensemble`
`us_trees`	Research-grade iNaturalist observation filtered to tree genera in the United States	`./us_trees`

Dataset format

All evaluation datasets were processed to the standard format and properties described in the table below. Key properties include location coordinates, label, and time period over which that label is considered applicable or "valid". In some cases, e.g., LCMAP, LUCAS, and Canada crops, multiple evaluation datasets were created using different hierarchies or combinations of labels. For regression datasets, there is no label_name column, and the label column records the measurement value. Change detection datasets include a number of additional columns to characterize "before" and "after" periods and labels, in addition to a general "change" / "no change" label.

Table: Evaluation dataset properties.

Property	Units	Notes
x	decimal	Longitude coordinate.
: : degrees : Must have at least 10-4 :
: : : precision to be :
: : : considered valid at ~10m :
: : : resolution. :
y	decimal	Latitude coordinate. Must
: : degrees : have at least 10-4 :
: : : precision to be :
: : : considered valid at ~10m :
: : : resolution. :
label	numeric	Column recording the
: : (int or : label or measurement :
: : float) : field used for evaluation :
: : : (one per dataset). For :
: : : classification, this :
: : : should be dense :
: : : sequential remapping of :
: : : ‘label_name’ values. For :
: : : regression, this should :
: : : be the (continuous) :
: : : measured/observed value :
label_name	str	(optional) This field can
: : : be used to preserve :
: : : values/codes from the :
: : : original dataset for :
: : : human-readability / :
: : : easier cross-checking and :
: : : visualization. Strongly :
: : : recommended for :
: : : classification evals, not :
: : : required for regression :
: : : evals. :
valid_time_start_ms	millis	Start and end times
: valid_time_end_ms : : defining the range over :
: : : which the :
: : : label/measurement is :
: : : valid (may be same for :
: : : instantaneous/single-date :
: : : measurements). This is :
: : : the period over-which the :
: : : embedding summary is :
: : : created and should not :
: : : extend more than 6 months :
: : : before or after the :
: : : support period. :
support_time_start_ms	millis	Start and end times
: support_time_end_ms : : defining a support period :
: : : for informing prediction. :
: : : This is the period over :
: : : which input data is :
: : : fetched for each row. It :
: : : must be no longer than 1 :
: : : year in length. :
split	str	Each label/observation
: : (train or : (row) should be assigned :
: : test) : to a fixed train/test :
: : : split. :
shard	numeric	{optional) Assign a shard
: : (int) : to each row for efficient :
: : : ingestion. A shard should :
: : : be associated with no :
: : : more than 2000 rows. :
label_before*	numeric	Integer label for
: : (int) : “before” class. :
label_before_name*	str	This is used to preserve
: : : “before” values/codes :
: : : from the original dataset :
: : : for human-readability / :
: : : easier cross-checking and :
: : : visualization :
label_after*	numeric	Integer label for “after”
: : (int) : class :
label_after_name*	str	This is used to preserve
: : : “after” values/codes from :
: : : the original dataset. :
valid_time_start_before_ms*	millis	Start and end times
: valid_time_end_before_ms* : : defining the range over :
: valid_time_start_after_ms* : : which the “before” and :
: valid_time_end_after_ms* : : “after” :
: : : labels/measurements are :
: : : valid (may be same for :
: : : instantaneous/single-date :
: : : measurements). These are :
: : : the periods over-which :
: : : embedding summaries are :
: : : created. :
support_time_start_before_ms*	millis	Start and end times
: support_time_end_before_ms* : : defining the before and :
: support_time_start_after_ms* : : after change support :
: support_time_end_after_ms* : : periods. :

* These fields are required for change detection datasets only

General pre-processing

In selecting evaluation datasets, we preferred point measurements or annotations over polygons, given that reasoning about labels for a specific point rather than over a larger area is more straightforward, and this simplifies sampling of feature vectors and/or mapped results for comparisons across approaches. We only selected datasets where point coordinate data (longitude, latitude) in decimal degrees was sufficiently precise relative to a nominal 10-meter resolution, i.e., at least four decimal points of precision (0.0001), which is about 11.1m at the equator. Point observations within each dataset were filtered to guarantee a minimum distance of 1.28 km between sampled points in order to reduce spatial autocorrelation between training and test sites (and this process will be hereafter referred to as “spatial proximity filtering”). Given our focus on temporal precision, we also required that labels have a clearly defined “valid period” over which the label could be reasonably applied, i.e., a range or instant (annual, monthly, single-date), and this period must intersect 2017 onward. We ensured that we had representation of different temporal aggregations across our final set of evaluations. Sample points were allocated to train and test splits such that the training datasets were balanced by class (or regularly spaced bins in the case of regression datasets), with no per-class sample exceeding 300 points and the remainder of the points allocated to an unbalanced test split. When possible, we used existing Google Earth Engine assets for publicly available datasets; otherwise, reference datasets were downloaded from archived sources. Additional details on sources and processing for individual datasets are provided in README files provided in nested directories.

Trial group configuration

Each dataset has an associated trial group configuration file. This is a JSON-syntax file that specifies which groups of trials should be performed during evaluation. For example, one configuration from africa_crop_mask_trial_groups.json) is json { "id": "africa_crop_mask_knn3_500x10", "repeats_n": 500, "samples_n": 10, "model_type": "knn", "metrics_type": "classification", "model_config": {"k": 3} }

This says that during the evaluation of the Sub-Saharan Africa Cropland Maps dataset, a group of 500 classification trials should be run. Each trial in that group will randomly select 10 samples from each class (selecting only from the train split), train a KNN model with k = 3, and evaluate the performance of that model on all the points in the test split. Evaluation of regression datasets is similar, except that samples_n specifies how many samples should be drawn from each histogram bin, using the partition column.

License and Disclaimer

Copyright 2025 Google LLC All software is licensed under the Apache License, Version 2.0 (Apache 2.0); you may not use this file except in compliance with the Apache 2.0 license. You may obtain a copy of the Apache 2.0 license at: https://www.apache.org/licenses/LICENSE-2.0

All other materials, except as set out below, are licensed under the Creative Commons Attribution 4.0 International License (CC-BY). You may obtain a copy of the CC-BY license at: https://creativecommons.org/licenses/by/4.0/legalcode.

iNaturalist is licensed under a Creative Commons Attribution Non-Commercial 4.0 License (CC-BY-NC). You may obtain a copy of the CC-BY-NC license at https://creativecommons.org/licenses/by-nc/4.0/deed.en.
Canadian AAFC Annual Crop Inventory from the Canadian AAFC (Agriculture and Agri-Food Canada) is licensed under the Open Government Licence Canada. You may obtain a copy of the licence at https://open.canada.ca/en/open-government-licence-canada.

Attribution for all datasets listed in the ‘Overview’ section above is set out in their respective README files.

Unless required by applicable law or agreed to in writing, all software and materials distributed here under the Apache 2.0 or CC-BY licenses are distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the licenses for the specific language governing permissions and limitations under those licenses.

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