IO MONITOR

10m Land Cover

On-demand seasonal or annual insights with comprehensive land cover classes

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Unlock insights

Monitor a growing range of seasonal land cover types for insights at 10 meter resolution.

Inform decisions

Receive reports on where and when changes are occurring in your area of interest.

Continuous updates

Compare seasonal land cover maps from 2017 up to the most recent Sentinel-2 satellite imagery for a time series of insights.

Simple pricing

Standard pricing up to 15,000km² with tiered pricing available for larger areas.

Spotlighting change in the areas you care about

Innovative AI-powered methods automate and accelerate mapping and monitoring in near-real-time. IO Monitor uses a unique deep learning approach to classify land use and land cover categories globally using the state-of-the-art Copernicus Sentinel-2 imagery.

Land Use Land Cover Map over Bradenton, Florida. Derived from Sentinel-2 satellite imagery

10m Land Cover classes

Monitor what matters to you with custom orders

More detail with 15 land cover classes

User-defined areas of interest

Track historical patterns with seasonal variations

Data creation and delivery within hours

Customize date ranges based on local seasons

Easy integration with Esri ArcGIS products

Email, Microsoft Azure, or Amazon AWS delivery

Data for better decision making

Detect patterns to enable data-driven decision-making with a clear and up-to-date understanding of any place on Earth.

example of food and farming through the use of IOs land use land cover map
San Juan del Piari, Bolivia

Assess risks to food systems and surrounding ecosystems

Identify changes in agriculture practices and fallow or abandoned crops with Active or Inactive Cropland classes.

Food and Farming Sample Data
example of food and farming through the use of IOs land use land cover map
Mato Grosso, Brazil

Identify risks for supply chains

See where, when, and how much supply chains affect loss or gain of critical natural ecosystems through changes in Dense Rangeland, Sparse Rangeland, and Trees classes

Sustainable Sourcing Sample Data
example of food and farming through the use of IOs land use land cover map
Xiong’an, China

Reveal the patterns of urban development

See the distribution of populations and the magnitude of urban expansion with High Density and Low Density Built Area classes.

Urban Sprawl and Human Expansion Sample Data
example of food and farming through the use of IOs land use land cover map
Dnipro Reservoir in Ukraine

Understand risks to key ecosystems and watersheds

Assess risk of flood or drought, and impacts on critical watersheds and downstream communities based on water levels and vegetation density.

Ecosystems and Watersheds Sample Data

Our Land Cover Maps

Find the map that helps you meet your goals, by comparing Impact Observatory's map offerings.

Maps for Good10m Land Cover3m Land Cover
Spatial Resolution10 meters10 meters3 meters
Spatial ExtentCustom area of interestCustom area of interestCustom area of interest
Date RangeAnnualCustom seasonal or annual datesCustom seasonal or annual dates
Length of Archive2017–20232017–Present2020–Present
Best Suited forBroad long term land cover analysisMonitoring dense and sparse vegetation types and built areas, active and inactive croplands, and seasonal water patterns Monitoring land cover types within urban areas including roads, buildings, mixed vegetation, other built areas
Categories 9 classes15 classes17 classes
Imagery SourcesSentinel-2Sentinel-2PlanetScope & Sentinel-2
Available File TypesCloud Optimized GeoTIFF (COG), PNG and CSVCloud Optimized GeoTIFF (COG), PNG and CSVCloud Optimized GeoTIFF (COG), PNG and CSV
PriceFree$2/km2$7.50/km2

Dive into the Details

Explore additional resources to improve your understanding of our 10m Land Cover offering

Documentation

Additional product details, tutorials, and release notes.

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10m Land Cover Classes

Get detailed descriptions of each of our land cover classes.

Discover your possibilities!

Create your own custom map or talk to one of our sales team members

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