ESRI Global Land Use Land Cover

Date modified: 02 August 2022

Product Overview


The ESRI global land use land cover (LULC) map at 10 meter resolution was developed by the Impact Observatory (IO) with the Environmental Systems Research Institute (ESRI) and in patnership with Microsoft AI for Earth.

Digital Earth Africa provides free and open access to a copy of the ESRI/IO Land Cover product over Africa.

A Jupyter Notebook which demonstrates loading and using landcover datasets in the Sandbox is also available.


Couverture spatiale et temporelle

Relevant metadata for the ESRI/IO Land Cover product can be viewed on the DE Africa Metadata Explorer.

Table 1: ESRI/IO Land Cover product specifications


Nom du produit


Cell size - X (degrees)

0.0000898° (~10m)

Cell size - Y (degrees)

0.0000898° (~10m)

Système de référence des coordonnées

Product has no default CRS.

Résolution temporelle


Plage temporelle

2017 - 2021

Fréquence de mise à jour


The specific temporal and geographic extents for the product can be explored as an interactive map on the DE Africa Metadata Explorer. Data is available for the region shaded in blue.

Figure 1: ESRI/IO Land Cover product geographic extent

ESRI LULC Geographic Extent


Table 2: ESRI/IO Land Cover product measurements

ID de la bande



Type de données

Pas de données\(^\dagger\)


Land cover classification





The ESRI/IO global LULC map is derived from the European Space Agency (ESA) Sentinel-2 imagery. A deep learning AI land classification model was trained using a massive training dataset curated by the National Geographic Society. This dataset contains over 5 billion hand-labelled Sentinel-2 imagery pixels, from 6 bands of surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. The pixels were sampled from over 20,000 sites distributed across all major biomes of the world.

The UNET, deep learning model was applied to multiple ESA Sentinel-2 scenes across a year, accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. The images were classified into 9 discrete land use/land cover classes. The LULC predictions were then composited to generate a representative map of the year.

The original Esri 2020 Land Cover collection uses 10 classes and an older version of the underlying deep learning model. This new map uses an updated model from the 10-class model and combines Grass(formerly class 3) and Scrub (formerly class 6) into a single Rangeland class (class 11).

Médias et exemples d’images

Figure 2: ESRI/IO Land Cover classification for Madagascar in 2020

IO LULC Geographic Extent


  1. Karra, C. Kontgis, Z. Statman-Weil, J. C. Mazzariello, M. Mathis and S. P. Brumby, « Global land use / land cover with Sentinel 2 and deep learning, » 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 4704-4707, doi: 10.1109/IGARSS47720.2021.9553499.

Kontgis, C. (2021, June 24). Mapping the world in unprecedented detail

License and Acknowledgements

This data is licensed under a Creative Commons by Attribution (CC BY 4.0) license.

Credits: Impact Observatory, Microsoft, and Esri

Data Acess

Services Web de l’OGC (OWS)

The ESRI/IO Land Cover product io_lulc is available through the Digital Earth Africa’s OWS.

Tableau 3 : Détails de l’accès aux données OWS.

Détails de l’OWS


DE Africa Services

URL des services cartographiques Web (WMS)

URL du service de couverture Web (WCS)

Nom de la couche


Les détails de Digital Earth Africa OWS peuvent être trouvés sur

Pour obtenir des instructions sur la manière de se connecter à OWS, consultez ce tutoriel.

Open Data Cube (ODC)

The ESRI/IO Land Cover product can be accessed through the Digital Earth Africa ODC API, which is available through the Digital Earth Africa Sandbox.

ODC product name: io_lulc

The io_lulc product has only one specific band of data which can be called by using the default name, data, or by the band’s alternative name, classification, as listed in the table below. ODC Datacube.load commands without specified bands will load the data band.

Table 4: ODC product io_lulc band names.

Nom du groupe

Noms alternatifs

Valeur de remplissage




Informations techniques

Accuracy assessment of the ESRI 2020 Land Cover

The original Esri 2020 Land Cover collection uses 10 classes and an older version of the underlying deep learning model.

Following best practices for accuracy assessment, Impact Observatory adjusted the acreage estimates for each land cover class in the ESRI 2020 Land Cover product using its respective user’s accuracy as computed from the comparison to the validation set. The deep learning land cover classification model achieved an overall accuracy of 86% on the validation set. This approach also allowed Impact Observatory to produce a 95% confidence interval for each acreage estimate, providing users with a clearer picture of the accuracy and total area for each class. (From the ESRI release page)

Figure 3: Confusion matrix of pixel counts evaluated against « three expert strict » gold standard validation tiles

IO LULC Geographic Extent

Image courtesy of ESRI.