Introducing RasterFlow: a planetary scale inference engine for Earth Intelligence LEARN MORE

On-demand inference built for aerial imagery

Run models for aerial imagery on-demand and work with insights using the tools you know and love

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Why Wherobots

Analyzing earth observation data is often limited by human interactive capacity or data processing constraints.

With Wherobots, you can process massive EO data using managed or open models, and easily complete your solution with SQL or Python.

Enhance your spatial analysis with your own or hosted GeoAI and ML models, on-demand within Wherobots.

On-demand spatial computing is ready for various geospatial data types, at any scale.

Complete your analysis using over 300 built-in vector and raster functions, and visualization support in a unified notebook environment.

Use pre-trained models or bring your own-min
Use pre-trained models, or bring your own

Use pre-trained models for segmentation, object detection, and classification from the Allen AI Institute, or import your own model using the STAC Machine Learning Model specification.

Use models with public and commercial imagery-min
Use models with public and commercial imagery

Leverage open and commercial data using any STAC collection to analyze gigabytes, terabytes, or petabytes of imagery.

Leverage JupyterLab notebook
Leverage JupyterLab, Apache Airflow, or your favorite tools.

Work with the familiar tools you know, including popular notebooks and orchestration tools, or your favorite command line interface (CLI) via the Wherobots API.

From a developer perspective, having data, algorithms and compute (and to be presented with a Spark/Sedona context in a Jupiter notebook on startup) combined in one platform is extremely powerful, comparable in many respects to Google Earth Engine, but with much greater guarantees of, and control over, job completion.
John Powell, Senior Geospatial Data Engineer at Addresscloud
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