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This gallery provides ready-to-use configuration examples for visualizing common geospatial data types in Microsoft Planetary Computer Pro. Each example includes comprehensive JSON configurations for mosaics, render options, tile settings, and SpatioTemporal Asset Catalog (STAC) collection metadata that you can adapt for your own datasets.
Table of contents
- Prerequisites
- How to use these examples
- Sentinel-2-l2a Multi-Spectral Imagery
- The National Agriculture Imagery Program Aerial Imagery
- Umbra Synthetic Aperture Radar Imagery
- Impact Observatory Land Use/Land Cover 9-class
Prerequisites
Before using these examples, you should have:
- A deployed Microsoft Planetary Computer Pro GeoCatalog resource
- Basic familiarity with STAC concepts and collection configuration
- Data ingested into a STAC collection
How to use these examples
Each example in this gallery includes:
- Description and context - Information about the data source and visualization approach
- Visual example - Screenshot of the rendered data in the Explorer
- Complete configuration settings organized in tabs:
- Mosaic - How to filter and select items for display
- Render Options - How to style and visualize the data
- Tile Settings - How to optimize display parameters
- STAC Collection - The underlying collection metadata structure
To apply these examples to your own data:
- Create a new collection in your GeoCatalog
- Ingest Data into the collection.
- Navigate to the collection's configuration page
- Modify the example JSON to match your dataset's specific bands, assets, and properties
- Apply the configurations to your collection
- View the results in the Explorer
Sentinel-2-l2a collection configuration
Sentinel-2 is a high-resolution, multi-spectral imaging mission from the European Space Agency (ESA) as part of the Copernicus Program.
Sentinel-2 configuration details
Mosaic configuration
This mosaic configuration tells the Explorer to display the most recent Sentinel-2 images from the collection, but only those images with cloud cover less than or equal to 40%. The Common Query Language (CQL) filter ensures that only relatively clear images are included, making the visualization more useful for most applications. Each mosaic entry can define different criteria for selecting and combining images, and this mosaic example uses a single "default" mosaic focused on recent, low-cloud imagery.
[
{
"id": "default",
"name": "Most recent available",
"description": "Most recent available imagery in this Sentinel-2 collection",
"cql": [
{
"op": "<=",
"args": [
{
"property": "eo:cloud_cover"
},
40
]
}
]
}
]
The National Agriculture Imagery Program collection configuration
The National Agriculture Imagery Program (NAIP) provides high-resolution aerial imagery across the United States. The USDA Farm Service Agency captures this NAIP imagery at least every three years.
NAIP data offers excellent detail with spatial resolutions ranging from 0.3 meter to 1 meter per pixel. The imagery is stored in cloud-optimized GeoTIFF format for efficient access and processing.
Each NAIP image contains four spectral bands:
- Red
- Green
- Blue
- Near-Infrared (NIR)
All four bands are stored together as a single multi-band asset. This band structure enables several types of analysis:
- Natural color visualization uses the RGB bands (1-3) to create images that look similar to what the human eye sees
- Color infrared analysis combines NIR, Red, and Green bands to assess vegetation health
- NDVI calculations use the formula (NIR-Red)/(NIR+Red) to measure vegetation density and health
NAIP configuration details
Mosaic configuration
The mosaic configuration defines how images are combined when displayed in the Explorer, this NAIP collection uses the default settings.
[
{
"id": "default",
"name": "Default",
"description": "",
"cql": []
}
]
Umbra SAR imagery collection configuration
Umbra's Synthetic Aperture Radar (SAR) imagery uses radar signals transmitted from satellites to create high-resolution images of the Earth's surface, capable of seeing through clouds, darkness, and weather conditions that would block traditional optical satellites. This SAR technology is valuable for monitoring infrastructure, detecting changes in urban areas, tracking ships and vehicles, and assessing damage after natural disasters, as it can capture detailed images at any time of day or night regardless of weather conditions.
SAR configuration details
Mosaic configuration
This SAR collection is the default mosaic configuration.
[
{
"id": "default",
"name": "Default",
"description": "",
"cql": []
}
]
Impact Observatory Land Use/Land Cover 9-class collection configuration
The Impact Observatory Land Use/Land Cover 9-Class dataset provides annual global maps of land use and land cover (LULC). This dataset was generated using billions of human-labeled pixels to train a deep learning model for land classification, applied to Sentinel-2 imagery at 10-meter resolution.
The 9-class system includes: Water, Trees, Flooded vegetation, Crops, Built area, Bare ground, Snow/ice, Clouds, and Rangeland. This updated classification model combines the previously separate Grass and Scrub classes into a single Rangeland class, providing more consistent classification across the time series.
Each annual map represents a composite of LULC predictions throughout the year, with an assessed average accuracy of over 75%. The data is valuable for monitoring land use changes, tracking deforestation, urban expansion, and agricultural patterns at a global scale.
Land use/land cover configuration details
Mosaic configuration
The mosaic configuration for this collection provides temporal filtering options, allowing users to view land cover data for specific years. Each mosaic definition filters the data to show only items from a particular year using Common Query Language (CQL) expressions. This temporal filtering allows users to compare land cover changes year-over-year or focus on a specific time period of interest
The configuration includes six separate mosaic options covering 2017-2022:
- Temporal Filtering: Each mosaic uses the
anyinteractsoperator to filter items where thedatetimeproperty intersects with a specific year's date range - Date Ranges: Each year's filter spans from January 1 to December 31 of that specific year (
2022-01-01T23:59:59Zto2022-12-31T23:59:59Z)
This temporal filtering approach is valuable for land cover analysis, as it enables users to track changes in land use patterns, monitor deforestation or reforestation, observe urban expansion, and assess the impact of natural disasters or human activities over time.
[
{
"id": "2022",
"name": "2022",
"description": "2022 Use/Land Cover",
"cql": [
{
"op": "anyinteracts",
"args": [
{
"property": "datetime"
},
{
"interval": [
"2022-01-01T23:59:59Z",
"2022-12-31T23:59:59Z"
]
}
]
}
]
},
{
"id": "2021",
"name": "2021",
"description": "2021 Use/Land Cover",
"cql": [
{
"op": "anyinteracts",
"args": [
{
"property": "datetime"
},
{
"interval": [
"2021-01-01T23:59:59Z",
"2021-12-31T23:59:59Z"
]
}
]
}
]
},
{
"id": "2020",
"name": "2020",
"description": "2020 Use/Land Cover",
"cql": [
{
"op": "anyinteracts",
"args": [
{
"property": "datetime"
},
{
"interval": [
"2020-01-01T23:59:59Z",
"2020-12-31T23:59:59Z"
]
}
]
}
]
},
{
"id": "2019",
"name": "2019",
"description": "2019 Use/Land Cover",
"cql": [
{
"op": "anyinteracts",
"args": [
{
"property": "datetime"
},
{
"interval": [
"2019-01-01T23:59:59Z",
"2019-12-31T23:59:59Z"
]
}
]
}
]
},
{
"id": "2018",
"name": "2018",
"description": "2018 Land Use/Land Cover",
"cql": [
{
"op": "anyinteracts",
"args": [
{
"property": "datetime"
},
{
"interval": [
"2018-01-01T23:59:59Z",
"2018-12-31T23:59:59Z"
]
}
]
}
]
},
{
"id": "2017",
"name": "2017",
"description": "2017 Land Use/Land Cover",
"cql": [
{
"op": "anyinteracts",
"args": [
{
"property": "datetime"
},
{
"interval": [
"2017-01-01T23:59:59Z",
"2017-12-31T23:59:59Z"
]
}
]
}
]
}
]