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 Models
Learn about built-in models and how to bring your own.
Run as a Job
Submit RasterFlow workflows as automated Job Runs.
Why choose RasterFlow?
RasterFlow provides a managed workflow execution environment for geospatial raster processing tasks. It abstracts away the complexity of raster pipelines and distributed computing, allowing you to focus on your analysis rather than data engineering and infrastructure management. Key capabilities include:Planetary-scale processing
Planetary-scale processing
Process raster data at massive scale with optimized chunking, sharding, and parallel processing
Simple, high-level API
Simple, high-level API
Abstract away the complexity with pre-configured datasets and models—or bring your own
Build mosaics
Build mosaics
Combine multiple raster datasets into unified, analysis-ready mosaics
Run model inference
Run model inference
Apply machine learning models to massive raster datasets at scale
Vectorize results
Vectorize results
Convert raster predictions into vector geometries for spatial analysis
Custom workflows
Custom workflows
Build flexible pipelines with your own data, models, and processing parameters
Standard format support
Standard format support
Work with GeoTIFF, Zarr, and GeoParquet formats
Key concepts
The following concepts are fundamental to understanding how RasterFlow works:Mosaics
Mosaics
Mosaics are spatially-aligned raster datasets stored in Zarr format. RasterFlow can build mosaics from aerial and satellite imagery to prepare them for model inference. This process can combine one or more datasets across an Area of Interest (AOI) and a temporal dimension to create a single seamless input mosaic for inference.RasterFlow has workflows for building mosaics from the built-in datasets or your own imagery.
Model inference
Model inference
Run computer vision models on raster data at scale:
- Semantic Segmentation: Classify each pixel (e.g., land cover mapping)
- Regression: Predict continuous values (e.g., canopy height estimation in meters)
- Patch-based Processing: Handle large mosaics by dividing into manageable patches
Vectorization
Vectorization
Convert raster predictions to vector geometries:
- Threshold-based: Binarize continuous predictions
- Polygonization: Create polygon features from classified pixels
- Coordinate transformation: Reproject to desired CRS (e.g., WGS84)
Choosing a runtime size
RasterFlow manages its own compute resources for raster processing, so the Wherobots Runtime size you select does not affect RasterFlow workflow performance. You should generally use the Micro runtime for RasterFlow workloads to minimize cost. The only exception is if you plan to also perform vector processing with WherobotsDB (e.g., using SedonaContext for spatial SQL queries) in the same job or notebook session. In that case, choose a runtime size appropriate for your WherobotsDB workload — the RasterFlow portions of the workflow will still be unaffected by the runtime size selection.Get started
To get started, login to Wherobots Cloud and try out one of the built-in models by running a RasterFlow notebook in the Model Hub.
Agricultural Field Mapping
Detect field boundaries from Sentinel-2 imagery and segment crop fields across regions using the Fields of the World model
Urban Infrastructure Detection
Identify sidewalks, crosswalks, and pedestrian pathways from high-resolution aerial imagery using the Tile2Net model
Canopy Height Estimation
Predict tree canopy heights from aerial imagery to monitor forest health and vegetation structure using the Meta CHM v1 model
Rural Road Detection
Identify roads in rural environments and map road networks using the ChesapeakeRSC model
RasterFlow is currently in Private Preview. Wherobots is rolling out RasterFlow to a select group of Organizations. If you are interested in gaining early access to these new capabilities and helping shape the future of the product, register your interest here.
| 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 |
Next steps
- Try out the pre-configured model solutions notebooks in the Model Hub in Wherobots Cloud
- Run RasterFlow as a Job for automated, production-scale processing
- Explore the Client API Reference to learn about all available methods
- Review Data Models to understand configuration options
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