Land Cover 2050 - Global
Predicted global land cover for year 2050, generated by Clark Labs. The prediction was modeled globally.
Use this global model layer when performing analysis across continents. This layer displays a global land cover map and model for the year 2050 at a pixel resolution of 300m. ESA CCI land cover from the years 2010 and 2018 were used to create this prediction.
Variable mapped: Projected land cover in 2050.
Data Projection: Cylindrical Equal Area
Mosaic Projection: Cylindrical Equal Area
Extent: Global
Cell Size: 300m
Source Type: Thematic
Visible Scale: 1:50,000 and smaller
Source: Clark University
Publication date: April 2021
What you can do with this layer?
This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.”
This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.
Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:
There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest).
Land Cover 2050 - Global
Land Cover 2050 - Regional
Land Cover 2050 - Country
Land Cover Vulnerability to Change 2050 Global
Land Cover Vulnerability to Change 2050 Regional
Land Cover Vulnerability to Change 2050 Country
What these layers model (and what they don’t model)
The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.
The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.
Quantitative Variables used to create Models
- Biomass
- Crop Suitability
- Distance to Airports
- Distance to Cropland 2010
- Distance to Primary Roads
- Distance to Railroads
- Distance to Secondary Roads
- Distance to Settled Areas
- Distance to Urban 2010
- Elevation
- GDP
- Human Influence Index
- Population Density
- Precipitation
- Regions
- Slope
- Temperature
Qualitative Variables used to create Models
- Biomes
- Ecoregions
- Irrigated Crops
- Protected Areas
- Provinces
- Rainfed Crops
- Soil Classification
- Soil Depth
- Soil Drainage
- Soil pH
- Soil Texture
Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.
As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.
Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050.
39 Small Countries Not Modeled
There were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:
- Andorra
- Antigua and Barbuda
- Barbados
- Cape Verde
- Comoros
- Cook Islands
- Djibouti
- Dominica
- Faroe Islands
- French Guyana
- French Polynesia
- Gibraltar
- Grenada
- Guam
- Guyana
- Iceland
- Jan Mayen
- Kiribati
- Liechtenstein
- Luxembourg
- Maldives
- Malta
- Marshall Islands
- Micronesia, Federated States of
- Moldova
- Monaco
- Nauru
- Saint Kitts and Nevis
- Saint Lucia
- Saint Vincent and the Grenadines
- Samoa
- San Marino
- Seychelles
- Suriname
- Svalbard
- The Bahamas
- Tonga
- Tuvalu
- Vatican City
Index to land cover values in this dataset:
The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover.
1 Mostly Cropland 2 Grassland, Scrub, or Shrub 3 Mostly Deciduous Forest 4 Mostly Needleleaf/Evergreen Forest 5 Sparse Vegetation 6 Bare Area 7 Swampy or Often Flooded Vegetation 8 Artificial Surface or Urban Area 9 Surface Water 10 Permanent Snow and Ice
Datasets available for download
-
Land_Cover_Projection_2050Layer
Layer None via ArcGIS REST API
-
Land_Cover_Projection_2050 (Explore)HTML
Explore Land_Cover_Projection_2050 on ArcGIS Hub
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Image ServiceHTML
Linked dataset resource
Additional Info
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| Last Updated | March 24, 2026, 21:07 (UTC) |
| Created | March 24, 2026, 21:07 (UTC) |
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Domain / Topic
Domain or topic of the dataset being cataloged.
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Title
Title for the Dataset.
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Land Cover 2050 - Global |
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Description
A description of the dataset.
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Predicted global land cover for year 2050, generated by Clark Labs. The prediction was modeled globally.
Use this global model layer when performing analysis across continents. This layer displays a global land cover map and model for the year 2050 at a pixel resolution of 300m. ESA CCI land cover from the years 2010 and 2018 were used to create this prediction.
Qualitative Variables used to create Models
Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu. As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change. Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050. 39 Small Countries Not Modeled There were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:
Index to land cover values in this dataset: The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover. 1 Mostly Cropland 2 Grassland, Scrub, or Shrub 3 Mostly Deciduous Forest 4 Mostly Needleleaf/Evergreen Forest 5 Sparse Vegetation 6 Bare Area 7 Swampy or Often Flooded Vegetation 8 Artificial Surface or Urban Area 9 Surface Water 10 Permanent Snow and Ice |
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Format (CSV, XLS, TXT, PDF, etc)
File format of the dataset.
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Dataset Size
Dataset size in megabytes.
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0.08 |
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Metadata Identifier
Metadata identifier – can be used as the unique identifier for catalogue entry
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Published Date
Published date of the dataset.
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2021-07-09 |
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Time Period Data Span (start date)
Start date of the data in the dataset.
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Time Period Data Span (end date)
End date of time data in the dataset.
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GeoSpatial Area Data Span
A spatial region or named place the dataset covers.
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Access category
Type of access granted for the dataset (open, closed, service, etc).
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public |
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License
License used to access the dataset.
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Creative Commons Attribution |
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Limits on use
Limits on use of data.
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Location
Location of the dataset.
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https://climate.esri.ca/datasets/esri::land-cover-2050-global |
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Data Service
Data service for accessing a dataset.
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Owner
Owner of the dataset.
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esri_environment |
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Contact Point
Who to contact regarding access?
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Esri |
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Contact Point Email
The email to contact regarding access?
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Publisher
Publisher of the dataset.
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Esri |
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Publisher Email
Email of the publisher.
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Author
Author of the dataset.
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esri_environment |
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Author Email
Email of the author.
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Accessed At
Date the data and metadata was accessed.
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2025-07-02 |
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Identifier
Unique identifier for the dataset.
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cee96e0ada6541d0bd3d67f3f8b5ce63 |
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Language
Language(s) of the dataset
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English |
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Link to dataset description
A URL to an external document describing the dataset.
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https://climate.esri.ca/datasets/esri::land-cover-2050-global |
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Persistent Identifier
Data is identified by a persistent identifier.
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Globally Unique Identifier
Data is identified by a persistent and globally unique identifier.
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Contains data about individuals
Does the data hold data about individuals?
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Contains data about identifiable individuals
Does the data hold identifiable data about individual?
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Contains Indigenous Data
Does the data hold data about Indigenous communities?
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Platform type of the source portal.
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Version
Version of the datatset
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None |
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Source
Source of the dataset.
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None |
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Version notes
Version notes about the dataset.
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Is version of another dataset
Link to dataset that it is a version of.
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Other versions
Link to datasets that are versions of it.
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Provenance Text
Provenance Text of the data.
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Esri |
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Provenance URL
Provenance URL of the data.
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Temporal resolution
Describes how granular the date/time data in the dataset is.
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GeoSpatial resolution in meters
Describes how granular (in meters) geospatial data is in the dataset.
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GeoSpatial resolution (in regions)
Describes how granular (in regions) geospatial data is in the dataset.
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Indigenous Community Permission
Who holds the Indigenous Community Permission. Who to contact regarding access to a dataset that has data about Indigenous communities.
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Community Permission
Community permission (who gave permission).
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The Indigenous communities the dataset is about
Indigenous communities from which data is derived.
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Number of data rows
If tabular dataset, total number of rows.
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4 |
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Number of data columns
If tabular dataset, total number of unique columns.
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16 |
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Number of data cells
If tabular dataset, total number of cells with data.
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64 |
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Number of data relations
If RDF dataset, total number of triples.
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Number of entities
If RDF dataset, total number of entities.
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Number of data properties
If RDF dataset, total number of unique properties used by the triples.
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Data quality
Describes the quality of the data in the dataset.
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Metric for data quality
A metric used to measure the quality of the data, such as missing values or invalid formats.
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