> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wherobots.com/llms.txt
> Use this file to discover all available pages before exploring further.

# RasterFlow Models

> Use RasterFlow to run inference on satellite imagery using the built-in models or your own PyTorch models.

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<CardGroup cols={2}>
  <Card title="RasterFlow Overview" icon="file-lines" href="/develop/rasterflow/">
    Learn about RasterFlow's key features and capabilities
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    Get started running RasterFlow in Wherobots.
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  <Card title="Reference" icon="code" href="/reference/rasterflow">
    Browse the RasterFlow API documentation
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  <Card title="RasterFlow Datasets" icon="satellite" href="/develop/rasterflow/rasterflow-datasets">
    Learn about built-in datasets and how to bring your own.
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## RasterFlow built-in models

RasterFlow includes curated, open-source models for common geospatial use cases.

| **Use Case**                       | **Capabilities**                                                                                                                                                              | **Example Application**                                                                                          | **Notebook**                                                                                      |
| ---------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- |
| **Agricultural Field Mapping**     | - Detect field boundaries from Sentinel-2 imagery<br /> - Segment crop fields across counties/regions<br /> - Convert raster predictions to vector geometries                 | Map all agricultural fields in Haskell County, Kansas using Sentinel-2 imagery and the Fields of the World model | [Try the Fields of the World notebook](https://cloud.wherobots.com/model-hub/fields-of-the-world) |
| **Urban Infrastructure Detection** | - Identify sidewalks, crosswalks, and pedestrian pathways<br /> - Generate detailed maps for urban planning<br /> - Analyze accessibility from high-resolution aerial imagery | Detect and map sidewalk networks in College Park, Maryland using 30cm NAIP imagery with the Tile2Net model       | [Try the Tile2Net notebook](https://cloud.wherobots.com/model-hub/tile2net)                       |
| **Canopy Height Estimation**       | - Predict tree canopy heights from aerial imagery<br /> - Monitor forest health and vegetation structure<br /> - Support conservation and urban forestry initiatives          | Estimate tree heights across Nashua, NH using 60cm NAIP imagery with the Meta CHM v1 model                       | [Try the Meta CHM v1 notebook](https://cloud.wherobots.com/model-hub/canopy-height)               |
| **Rural Road Detection**           | - Identify roads, especially in rural environments<br /> - Map road networks to support routing and navigation<br /> - Detect road network changes to keep maps up to date    | Detect roads in Maryland using 1m NAIP imagery with the ChesapeakeRSC model                                      | [Try the ChesapeakeRSC notebook](https://cloud.wherobots.com/model-hub/chesapeake-rsc)            |

## Running model inference

There are two options for running model inference in RasterFlow:

* Run an end-to-end workflow that ingests the required imagery, generates a mosaic and runs the model using a pre-configured recipe.  See [build\_and\_predict\_mosaic\_recipe()](https://docs.wherobots.com/reference/rasterflow/client#build-and-predict-mosaic-recipe) for more details.
* Run model inference on an existing mosaic.  See [predict\_mosaic()](https://docs.wherobots.com/reference/rasterflow/client#predict-mosaic) for more details.

## Vectorization of model outputs

Convert raster predictions to vector geometries for further spatial analysis:

Vectorization enables you to:

* Join with other vector datasets (e.g., cadastral data, yield records)
* Calculate area statistics for each field
* Perform spatial queries in WherobotsDB
* Export to standard GIS formats for visualization

See [vectorize\_mosaic()](https://docs.wherobots.com/reference/rasterflow/client#vectorize-mosaic) for more information.

## API reference

For detailed API documentation, see:

* [Client API Reference](/reference/rasterflow/client) - `RasterflowClient` methods
* [Data Models Reference](/reference/rasterflow/data-models) - Enums and configuration objects
* [Model Registry Reference](/reference/rasterflow/model-registry) - Working with model registries
* [Exceptions Reference](/reference/rasterflow/exceptions) - Error handling