Planetary-scale answers, unlocked.
A Hands-On Guide for Working with Large-Scale Spatial Data. Learn more.
GTM lead at Wherobots, building the Spatial Intelligence Cloud.
Wherobots and Felt Partner to Modernize Spatial Intelligence
We’re excited to announce Wherobots and Felt are partnering to enable data teams to innovate with physical world data and move beyond legacy GIS, using the modern spatial intelligence stack. The stack with Wherobots and Felt provides a cloud-native, spatial processing and collaborative mapping solution that accelerates innovation and time-to-insight across an organization. What is […]
How Aarden.ai Scaled Spatial Intelligence 300× Faster for Land Investments with Wherobots
When Aarden.ai emerged from stealth recently with $4M in funding to “empower landowners in data center and renewable energy deals,” the company joined a new wave of data and AI startups reimagining how physical-world data drives modern business. Their mission: help institutional land investors rapidly evaluate the value and potential uses of land across the country. […]
Wherobots Spatial Intelligence Engine Integrates with Databricks Unity Catalog for Spatial Data
Wherobots now federates with Databricks Unity Catalog to bring leading spatial data and GeoAI capabilities to the lakehouse.
Dekart Supports Wherobots as a Spatial SQL Engine
With Dekart now supporting Wherobots as a Spatial SQL engine, this combination creates a snappy, highly scalable query visualization experience for spatial data in your lakehouse.
Advancing the Integration of Map Data via Overture’s Global Entity Reference System and Wherobots
The general availability of Overture’s Global Entity Reference System (GERS) makes it a lot easier to build intelligence about features of our physical world.
Accelerating Vector and Raster Analysis at GeoPostcodes
How long is too long to wait for a data set to process? In the fast-paced world of data as a service, efficiency isn't just a nice-to-have; it's essential. But for GeoPostcodes, previous updates to the global population movement datasets took about 39 days. This was running on a headless QGIS instance in combination with PostGIS, combining population rasters with postal code boundaries.
