> ## 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 Datasets

> Learn about the built-in datasets and how you can use your own imagery with RasterFlow.

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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
  </Card>

  <Card title="Get Started" icon="flag-checkered" href="/develop/rasterflow#get-started">
    Get started running RasterFlow in Wherobots.
  </Card>

  <Card title="Reference" icon="code" href="/reference/rasterflow">
    Browse the RasterFlow API documentation
  </Card>

  <Card title="RasterFlow Models" icon="box" href="/develop/rasterflow/rasterflow-models">
    Learn about built-in models and how to bring your own.
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</CardGroup>

## RasterFlow built-in datasets

RasterFlow includes popular open datasets for common geospatial use cases.

### Sentinel-2

The Sentinel-2 dataset provides worldwide coverage, low resolution (10m to 30m, depending on the band) and 5-day revisit rates.

Sentinel-2 is suitable for use cases that do not require high-resolution imagery and benefit from its frequent revisit rates for change detection or analyzing seasonal trends.

RasterFlow has several built-in configurations for Sentinel-2. See the [`DatasetEnum`](/reference/rasterflow/data-models#datasetenum) reference for the full list of available datasets.

For an example of a workflow that uses Sentinel-2 imagery, see the [Fields of the World solution notebook](https://cloud.wherobots.com/model-hub/fields-of-the-world)
Additionally, for an example of how to build a Sentinel-2 mosaic using RasterFlow, see the [Sentinel-2 Mosaic example notebook](/tutorials/example-notebooks/RasterFlow-S2-Mosaic).

#### Cloud and quality filtering

RasterFlow automatically applies cloud and quality filtering when building Sentinel-2 mosaics. This ensures that the resulting composites contain only high-quality, cloud-free pixels.

##### Scene-level filtering

Scenes with 75% or more cloud cover are excluded from the mosaic. Only scenes with `cloud_cover < 75%` are considered as candidates for contributing pixels to the composite.

##### Pixel-level quality masking

Individual pixels are masked out based on the Sentinel-2 [Scene Classification Layer (SCL)](https://custom-scripts.sentinel-hub.com/custom-scripts/sentinel-2/scene-classification/) band. The following SCL values are masked:

| SCL Value | Classification           | Description                |
| --------- | ------------------------ | -------------------------- |
| 1         | Saturated or defective   | Sensor anomalies           |
| 3         | Cloud shadow             | Shadows cast by clouds     |
| 7         | Unclassified             | Unclassified pixels        |
| 8         | Cloud medium probability | Likely cloud pixels        |
| 9         | Cloud high probability   | Highly likely cloud pixels |
| 10        | Thin cirrus              | High-altitude ice clouds   |

<Note>
  The `S2_MED_WINDOWED_PIXEL` and `S2_BEST_SCENE_WINDOWED_PIXEL` datasets additionally mask out **snow or ice** (SCL value 11).
</Note>

After masking, RasterFlow computes a pixel-wise median (or selects the best scene, depending on the dataset) across the remaining valid observations to produce the final composite.

#### Seasonal date filtering

The planting and harvest season datasets (`S2_MED_PLANTING` and `S2_MED_HARVEST`) apply latitude-based heuristics to determine the optimal day-of-year (DOY) ranges for imagery selection. This ensures that composites represent the appropriate growing stage for agricultural analysis at a given location.

### National Agriculture Imagery Program (NAIP)

The National Agriculture Imagery Program (NAIP) captures high-resolution imagery (30cm to 1m) across the continental United States.  The imagery is captured regionally on a state by state basis and revisits states every two years.

NAIP is available at different resolutions from 30cm to 1m, depending on the year of capture and the specific state.  To ensure imagery in your Area of Interest (AOI) and time range are available in sufficient resolution, please refer to [this map](https://esri.maps.arcgis.com/apps/mapviewer/index.html?webmap=6cc0dcb225de4cb8aaa23c6a9cb59db8).

## 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