Utility Vegetation Management at Scale with Earth Intelligence

We mapped the trees tall enough to reach power lines in King County, WA and Shasta County, CA, using open NAIP imagery, Meta's canopy height model and Wherobots.

El Niño 2026, atmospheric rivers, and California’s burn scars: one SQL engine, two data models

7,713 mapped building footprints sit inside or within 500 m (1,640 ft) of the 42 Eaton basins USGS rates high hazard for debris flows, and 388 km (241 mi) of mapped road and path cross the low ground below the scar. WherobotsDB finds both in SQL: a join to the USGS basins for the buildings, and a raster vector join over elevation for the roads.

Measuring the Strait of Hormuz shutdown with Sentinel-2, WherobotsDB and Overture Maps

WherobotsDB read 719 Sentinel-2 scenes in place, fetched about 1.5 GB of the 151 GB, and joined the detections against Overture Maps land polygons to measure a 95% fall in traffic through the Strait of Hormuz corridor. Loading at Kharg and waiting at Fujairah held at 2025 levels, and the analysis cost about $63.

How to score every building in a state for catastrophe risk: an exploration project

Part 1 of a series: What’s possible? I didn’t start this project to build a real risk model, not one that can be used by an insurer tomorrow. But it is a workflow that an insurer can put into practice to create their own risk scores with Wherobots and their own expertise. The idea was […]

From the Spokane firestorm to all of Washington: real-time wildfire monitoring for under $50 a pass

The moment a satellite pass lands, this pipeline turns it into a burn-severity map in 14 minutes: real-time wildfire monitoring with Python and Spatial SQL that protects lives and assets, shown on the Spokane firestorm and scaled to the entire state of Washington.

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 […]

Change Detection Using AlphaEarth Foundations (Part 2)

Change detection with AlphaEarth Foundations measures how a place changes over time directly in embedding space, without hand-built spectral indices. This post scores temporal change using leave-one-out medoid distance and robust z-scoring per pixel, then runs it at scale on RasterFlow with a Colorado demonstration. The output flags where the landscape shifted and how strongly, ready to vectorize into geometries in WherobotsDB.