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Agriculture and forestry intelligence depends on spatial insights derived from satellite imagery, field sensors, and geospatial boundaries. Wherobots enables organizations to process multi-temporal raster data at continental scale, detect field boundaries, monitor crop health, and manage forest resources with purpose-built spatial AI.

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How to use these solutions

Treat these notebooks as data engineering patterns as you evaluate Wherobots for your business or organization:

Field Boundary Detection

Automatically delineate agricultural field boundaries from Sentinel-2 satellite imagery using RasterFlow’s Fields of the World model.

Crop & Vegetation Monitoring

Analyze multi-temporal satellite imagery to track vegetation indices, monitor crop growth stages, and detect stress across growing seasons.

Canopy Height & Forest Inventory

Estimate tree canopy height from aerial imagery to support forest inventory, carbon accounting, and timber volume calculations.

Land Cover Classification

Classify land cover types at regional or national scale using ESA WorldCover or custom raster datasets to inform land management decisions.

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Key Capabilities

Run the Fields of the World model through RasterFlow to detect and vectorize field boundaries from Sentinel-2 imagery. Generate ready-to-use polygon datasets for downstream analysis.

Detecting field boundaries

Delineate every field so metrics can be summarized per parcel.
Apply RasterFlow’s canopy height model to aerial imagery to produce high-resolution height maps. Use the results for forest carbon estimation, timber planning, or urban tree inventory.

Estimating canopy height

Predict tree heights from aerial imagery.
Calculate per-field or per-parcel statistics from raster layers, including mean NDVI, land cover percentages, and elevation summaries, using WherobotsDB zonal statistics.

Zonal statistics

Summarize raster values inside your own field boundaries.
Assemble Sentinel-2 or sub-meter NAIP imagery into spatially aligned mosaics across your area of interest and growing season, or bring your own GeoTIFFs.

Building Sentinel-2 mosaics

Assemble cloud-free imagery across an area and a season.

Building NAIP mosaics

Assemble sub-meter aerial imagery over your fields.

Bring your own rasters

Mosaic your own GeoTIFFs into analysis-ready imagery.
Compare satellite imagery across multiple dates to detect land-use change, deforestation, urban encroachment, or crop rotation patterns.

Change detection with RasterFlow

Compare imagery across dates and quantify what moved.

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Wherobots Advantage

Get started with Wherobots

Try Wherobots with your own geospatial data in minutes.