Planetary-scale answers, unlocked.
A Hands-On Guide for Working with Large-Scale Spatial Data. Learn more.
How well does SAM3 detect building footprints? We asked the Wherobots Spatial AI Coding Assistant
In a recent post, we showed how easy it is to use RasterFlow and Meta’s Segment Anything 3 Model (SAM3) to detect features in the physical world. A single end-to-end pipeline built a 133 GB NAIP mosaic of Marion County, Oregon, ran SAM3 against it with text prompts spanning eight classes, and produced approximately one […]
Introducing RasterFlow: a planetary scale inference engine for Earth Intelligence
RasterFlow takes insights and embeddings from satellite and overhead imagery datasets into Apache Iceberg tables, with ease and efficiency at any scale.
Introducing Scalability for GeoPandas in Apache Sedona
Learn about the new GeoPandas API for Apache Sedona, now available in Wherobots. This new API allows GeoPandas developers to seamlessly scale their analysis beyond what a single compute instance can provide, unlocking insights from large-scale datasets. This integration combines the Pythonic GeoPandas API with the distributed processing power of Apache Sedona.
