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

Discover

The world at your fingertips

Discover the data, models, notebooks, jobs and the compute you need to accelerate your analysis of the physical world.

Data in Place
Popular spatial datasets built-in, or bring your own via managed storage and by connecting directly to repos.
AI for Earth Models Built-In
Built-in open models for generating insights from aerial imagery.
Start with Solution Notebooks
Accelerate your development with Solution Notebooks based on real-world use cases.

Key capabilities

Solution Notebooks

Start with Solution Notebooks based on real-world use cases

Popular Spatial Datasets

Built-in datasets for Places, Buildings, Transportation and more, from Overture and Foursquare

Bring Your Own Data

Upload or register STAC collections, GeoParquet, or raster tiles

AI for Earth Model Hub

Find the right computer vision model to generate insights from aerial imagery

Collaborate With Your Team

Share your innovations with your colleagues

AI for Earth Models for Aerial Imagery and More

Run computer vision and advanced vector processing models to bring sense to unstructured data about activity on the physical world

Object detection

Detect objects in imagery using specific trained models or general purpose models with natural language prompts.

Segmentation

Delineate object boundaries in imagery, such as identifying crop fields and their boundaries.

Classification

Classify scenes in aerial imagery, such  as categorizing land cover as Pasture vs. Herbaceous Vegetation.

Map Matching

Snap noisy GPS data to road segments to simplify analysis of mobility data.

Integrations & Catalogs

Integrate your data sources

Use hosted datasets or integrate with your own catalogs and collections

Hosted Datasets

Popular, curated datasets built-in to Wherobots for seamless use.

High quality global datasets from Overture and Foursquare with places, buildings, transportation infrastructure and more.

Storage Integrations

Discover data in your cloud storage with Storage Integrations.

Connect AWS S3, Google Cloud Storage or Azure Blob Storage buckets and query immediately.

Databricks Unity Catalog

Integrate with Databricks Unity Catalog.

Register Unity as a catalog source. Browse schemas, tables, and views.

STAC Collections

Build references to Spatial Temporal Asset Catalog (STAC) collections

Register STAC URLs to index imagery and metadata for search & inference.

learning Resources

Next Steps

Planetary Scale
analytics

Build

Start using WherobotsDB with familiar Spark/Sedona syntax.

Automate

Orchestrate spatial workflows at scale using our Apache Airflow Provider.

AI for Earth

Easily deploy planetary scale AI on physical world data.

FAQ

Which datasets are built-in to Wherobots?

Wherobots has built-in datasets from Overture Maps Foundation and Foursquare including Places, Buildings, Transportation and more.
For more information, see the documentation.

Which GeoAI models are available in Wherobots?

Wherobots has built-in models for classification, object detection and segmentation. In addition, you can bring your own model for use within Wherobots Raster Inference. In addition we are working through the process of onboarding all models available in the TorchGEO model catalog. For more information, see the documentation.

How do I connect my own cloud storage?

With Wherobots S3 Storage Integration, you can connect and utilize your own public or private AWS S3 buckets while maintaining your existing governance and permissions.  Adding the required permissions for Wherobots, allows your organization to seamlessly use your buckets for reading and/or writing from Wherobots. This feature is available to Professional and Enterprise Edition organizations. For more information, see the documentation.

How do I access aerial imagery available through a SpatioTemporal Asset Catalog (STAC)?

WherobotsDB has a built-in STAC reader to connect to any STAC-compliant API to fetch and process geospatial data.  You can load STAC data directly into a Sedona DataFrame for further analysis and processing using Spark. For more information, see the documentation.
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