Private PreviewDocumentation 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 Overview
Learn about RasterFlow’s key features and capabilities
Get Started
Get started running RasterFlow in Wherobots.
Reference
Browse the RasterFlow API documentation
RasterFlow Datasets
Learn about built-in datasets and how to bring your own.
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 - Segment crop fields across counties/regions - 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 |
| Urban Infrastructure Detection | - Identify sidewalks, crosswalks, and pedestrian pathways - Generate detailed maps for urban planning - 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 |
| Canopy Height Estimation | - Predict tree canopy heights from aerial imagery - Monitor forest health and vegetation structure - 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 |
| Rural Road Detection | - Identify roads, especially in rural environments - Map road networks to support routing and navigation - 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 |
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() for more details.
- Run model inference on an existing mosaic. See 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
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