Wherobots

Spatial jobs from anywhere Python runs: CI/CD, notebooks, your local shell. One install, one API key. No AWS credentials. pip install wherobots-python-sdk bit.ly/4wkaPk1

Can AlphaEarth Foundations embeddings preserve interpretable structure after aggregation from pixels to field polygons? We used RasterFlow to build the mosaic and vectorized predictions, then an experimental zonal-stats step computed field-level means. bit.ly/4djVIhy

Can you score each annual AlphaEarth Foundations embedding by how much it stands out from the rest of its local time series? We tried it on the global AEF Zarr mosaic (via Source Cooperative), scoped to Colorado. Leave-one-out medoid + robust z-scoring. bit.ly/4dtzXx5

Which vacant Opportunity Zone parcels are 2+ acres, outside flood zones, within a 15-min drive of a hospital, and near amenities? We're answering live across Austin, Dallas, and Denver in one pipeline via Wherobots MCP server. Join us: bit.ly/42XOTxj

Wherobots is hiring. AI Context Engine for the Physical World. Fuse overhead imagery with vector geometries and reason about space, time, and geography at planetary scale. Open roles: - GeoAI Engineer - Cloud Infra - Enterprise AE bit.ly/4wd9ECL

We added Meta's SAM 3 to RasterFlow. Text prompt in, vector geometries out, no custom model training needed. We tested it on 133 GB of NAIP aerial imagery. Full pipeline ran in under an hour. Get the full breakdown: bit.ly/49aLrmE

Not every data platform was built for spatial workloads. We broke down 6 platforms, PostGIS, Snowflake, Databricks, BigQuery GIS, Apache Sedona, and Wherobots, so you know where each one fits and where it hits its ceiling. Full breakdown in the blog. bit.ly/4emPiAz

Which properties in your ZIP codes carry the highest compound climate risk? We're answering this live on 4/29. Wildfire, flood, and building risk scored across thousands of properties, built in front of you, all from VS Code. Come see how it works: bit.ly/4u8ulhm

Fields of the World (FTW) is a Taylor Geospatial effort to build globally consistent agricultural field-boundary data. For the 2024-2025 release, they partnered with Wherobots to run their latest model, PRUE, on RasterFlow. Read about it here: bit.ly/3QVVe9O

Most spatial data teams try to solve their entire pipeline with one tool. That's where the problems start. The Spatial Medallion Architecture uses Wherobots for heavy processing and PostGIS for fast delivery. Each tool does what it was built to do. bit.ly/3OWpHnD

Until Iceberg v3, geospatial columns didn't exist in the table format. Engineers stored geometry as binary blobs. No CRS metadata. No bounding-box statistics. Fragile, and it didn't travel across engines. Iceberg v3 fixes that. Get the full story: bit.ly/4mCTKxg

Buildings. Vehicles. Solar panels. Crop fields. You describe what you want to detect in a text prompt and SAM3 finds instances across your imagery. No model training. No infrastructure to manage. See RasterFlow + SAM3 live on April 22: bit.ly/4cqJ53V

EO embeddings have a lot of excitement around them. But storage costs, unclear fitness for use, and no shared benchmarks are still unsolved. Takeaways from the Geospatial Embeddings Workshop at Clark University: bit.ly/3Q86ijV

We just shipped three tools to bring AI-first spatial data workflows into VS Code, Cursor, Claude Code, and Kiro! Describe the problem. Get working code and real results back. bit.ly/48khK22

We built a notebook examining how agricultural fields evolve in AlphaEarth Foundations embeddings using RasterFlow capabilities: Zarr mosaic construction, PCA for dimensionality reduction, and a distance-over-time workflow for measuring embedding change. bit.ly/4e3mI72

Mobility data isn't just spatial data with timestamps attached. The order and timing of observations define movement. Most spatial systems were never built with that in mind. Here's where they break down. bit.ly/4dLzIxZ

Spatial data engines haven't kept up with the workloads being thrown at them. We fixed that. WherobotsDB next-gen is now GA: 3x faster queries, 46% lower cost than the next best alternative. Full benchmarks + free trial: bit.ly/4up1l5O

The biggest bottleneck in geospatial AI isn't the model architecture, it's data readiness. Join us for a hands-on session showing how to build automated pipelines that ingest, clean, and vectorize overhead imagery for immediate analysis. bit.ly/4r3AN7j

For 20 years, the answer to “Where should we store location data?” was PostGIS. The one-size-fits-all database is no longer enough. Learn how PostGIS, Wherobots and spatial data lakehouse fits together to meet today's needs without compromising cost. bit.ly/4bjtzGH

Geospatial data is shedding the "opaque blob" status. 🌍 Native support in Apache Parquet means geometry is now a first-class citizen in the columnar storage layer, no more workarounds for large-scale analytics. Read the breakdown: bit.ly/4kSNNLN