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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.
Wherobots RasterFlow is a powerful inference engine for large-scale raster processing and inferencing. It enables you to build mosaics from multiple raster data sources, run inference with computer vision models, and vectorize results—all with a simple, high-level API.

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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: Process raster data at massive scale with optimized chunking, sharding, and parallel processing
  • Simple, high-level API: Abstract away the complexity with pre-configured datasets and models—or bring your own
  • Build mosaics: Combine multiple raster datasets into unified, analysis-ready mosaics
  • Run model inference: Apply machine learning models to massive raster datasets at scale
  • Vectorize results: Convert raster predictions into vector geometries for spatial analysis
  • Custom workflows: Build flexible pipelines with your own data, models, and processing parameters
  • Standard format support: Work with GeoTIFF, Zarr, and GeoParquet formats

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Key concepts

The following concepts are fundamental to understanding how RasterFlow works:
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.
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
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)

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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. RasterFlow Model Hub
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Agricultural Field Mapping

Detect field boundaries from Sentinel-2 imagery and segment crop fields across regions using the Fields of the World model
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Urban Infrastructure Detection

Identify sidewalks, crosswalks, and pedestrian pathways from high-resolution aerial imagery using the Tile2Net model
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Canopy Height Estimation

Predict tree canopy heights from aerial imagery to monitor forest health and vegetation structure using the Meta CHM v1 model
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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.
The examples below demonstrate real-world applications that you can adapt to your own data and use cases. Each notebook provides a complete, working implementation to help you get started quickly.
Use CaseCapabilitiesExample ApplicationNotebook
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 modelTry 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 modelTry 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 modelTry 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 modelTry the ChesapeakeRSC notebook

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Next steps

  • Try out the pre-configured model solutions notebooks in the Model Hub in Wherobots Cloud
  • Explore the Client API Reference to learn about all available methods
  • Review Data Models to understand configuration options

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API reference

For detailed API documentation, see: