GLanCE: A Global Land Cover Training Dataset from 1984 to 2020

The GLanCE global database contains nearly 2 million training units spanning the period from 1984 to 2020 for seven primary and twelve secondary land cover classes. The database is medium spatial resolution (30 m), geographically and spectrally representative of all global ecoregions, and contains up to 23 land cover characteristics per sample. To support land cover change mapping, the dataset includes up to 36 years and information about abrupt and gradual land cover change.

Product Details

Visibility: Public
Owner: Boston University
Created: 26 Jun 2024
Last Updated: 21 Aug 2025

Product Contents

The Global Land Cover Estimation (GLanCE) project provides high-quality long-term records of land cover and land cover change at 30 m spatial resolution for the 21st century using the Landsat archive. The GLanCE land cover classification scheme is designed to be compatible with common land cover classification systems such as the IPCC land use categories for greenhouse gas inventory reporting and the FAO Land Cover Classification System (LCCS). As part of the GLanCE project, we present a new land cover training database that is designed to provide global coverage, ensure accuracy of land cover labels at 30 m spatial resolution, cover nearly four decades, and produce a geographically dense dataset.

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This repository contains a Cloud-Native Geospatial GeoParquet version as well as a GeoJSON version of the data. You can use the BROWSE link on the left hand side to navigate to the data and download it. You can also access the S3 bucket directly with S3 tools (aws cli, boto3, etc); the S3 URI is s3://us-west-2.opendata.source.coop/boston-university/bu-glance/

Citation & DOI

Stanimirova R., Tarrio K., Turlej K., McAvoy K., Stonebrook S., Hu K-T., Arévalo P., Bullock E.L., Zhang Y., Woodcock C.E., Olofsson P., Zhu Z., Barber C.P., Souza C., Chen S., Wang J.A., Mensah F., Calderón-Loor M., Hadjikakou M., Bryan B.A., Graesser J., Beyene D.L., Mutasha B., Siame S., Siampale A., and M.A. Friedl (2023) "A Global Land Cover Training Dataset from 1984 to 2020", Version 1.0, Radiant MLHub. [Date Accessed] https://doi.org/10.34911/rdnt.x4xfh3

Publication

Stanimirova, R., Tarrio, K., Turlej, K., McAvoy K., Stonebrook S., Hu K-T., Arévalo P., Bullock E.L., Zhang Y., Woodcock C.E., Olofsson P., Zhu Z., Barber C.P., Souza C., Chen S., Wang J.A., Mensah F., Calderón-Loor M., Hadjikakou M., Bryan B.A., Graesser J., Beyene D.L., Mutasha B., Siame S., Siampale A., and M.A. Friedl (2023) A global land cover training dataset from 1984 to 2020. Sci Data 10, 879 https://doi.org/10.1038/s41597-023-02798-5

Column Name	Description
Lat	Latitude
Lon	Longitude
Start_Year	Start year of segment, ranging from 1984 to 2020 (integer)
End_Year	End year of segment, ranging from 1984 to 2020 (integer)
Glance_Class_ID_level1	Level 1 land cover value (integer): 1 (Water), 2 (Ice/snow), 3 (Developed), 4 (Barren/sparsely vegetated), 5 (Trees), 6 (Shrub), and 7 (Herbaceous).
Glance_Class_ID_level2	Level 2 land cover value (integer): 1 (Water), 2 (Ice/snow), 3 (Developed), 4 (Soil), 5 (Rock), 6 (Beach/sand), 7 (Deciduous), 8 (Evergreen), 9 (Mixed), 10 (Shrub), 11 (Grassland), 12 (Agriculture), and 13 (Moss/lichen). NaN values present.
Leaf_Type	Tree leaf type: broadleaf (1), needleleaf (2), and mixed (3). NaN values present.
Impervious_Percent	Impervious percent for developed samples: low 0%-30% (1), medium 30%-60% (2), and high 60%-100% (3). NaN values present.
Tree_Location	Binary integer indicating whether trees are on the interior (0) or edge (1) of a forest. NaN values present.
Veg_Density	Vegetation density for trees and shrubs: sparse 0%-30% (1), open 30%-60% (2), and closed 60%-100% (3). NaN values present.
Veg_Modifier	Vegetation modifiers, which can include one or more of the following: Cropland, Plantation, Wetland, Riparian/Flood, Mangrove, Greenhouse, and Trees/Shrub Present. NaN values present.
Segment_Type	Indicates whether a segment is stable (0) or transitional (1). See Section 1 for a detailed description. Land cover for transitional segments is recorded at both the beginning and end of the time segment - typically the first and last three years. NaN values present.
Change	Indicates presence (1) or absence (0) of land cover change for Level 1 land cover labels. Includes both abrupt change and gradual change (transitional segments (1) from the Segment_Type attribute) if it happened at any time for that training unit.
LC_Confidence	Interpreter confidence in the Level 1 land cover label from 1 (lowest) to 3 (highest). NaN values present.
Level1_Ecoregion	Ecoregion Level 1 number based on World Wildlife Fund definitions. For North America we used ecoregions based on the Environmental Protection Agency’s Ecoregions of North America product.
Level2_Ecoregion	Ecoregion Level 2 number based on the Environmental Protection Agency’s Ecoregions of North America product. This field is available only for North America and is assigned a value of 0 for all other continents.
Continent_Code	Assigned continent number: North America (1), South America (2), Africa (3), Europe (4), Asia (5), and Oceania (6).
Dataset_Code	Assigned dataset number: 1, 2, 3, 4, 5, 902, 999, 700, 701, 702, 703, 704, 705, 706, and 707. Numbers correspond to each Dataset as follows: STEP, CLUSTERING, LCMAP, ABoVE, MapBiomas, Feedback, Training_augment, MODIS_algo, GeoWiki, RadEarth, Collaborator_data, BU_team_collected, GLC30, LUCAS, ASB_crop. For details see Scientific Data publication.
Glance_ID	Unique ID for each sample.
ID	ID for each unique combination of latitude and longitude. Change units have the same ID but different Glance_ID.

GLanCE: A Global Land Cover Training Dataset from 1984 to 2020

GLanCE: A Global Land Cover Training Dataset from 1984 to 2020

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