RasterFlow Overview
Learn about RasterFlow’s key features and capabilities
Get Started
Get started running RasterFlow in Wherobots.
Reference
Browse the RasterFlow API documentation
RasterFlow Models
Learn about built-in models and how to bring your own.
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 theDatasetEnum 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
Additionally, for an example of how to build a Sentinel-2 mosaic using RasterFlow, see the Sentinel-2 Mosaic example notebook.
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 withcloud_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) 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 |
The
S2_MED_WINDOWED_PIXEL and S2_BEST_SCENE_WINDOWED_PIXEL datasets additionally mask out snow or ice (SCL value 11).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.API reference
For detailed API documentation, see:- Client API Reference -
RasterflowClientmethods - Data Models Reference - Enums and configuration objects
- Model Registry Reference - Working with model registries
- Exceptions Reference - Error handling