Planetary-scale answers, unlocked.
A Hands-On Guide for Working with Large-Scale Spatial Data. Learn more.
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Apache Sedona has reached ⭐ 2M+ downloads ⭐ in the past month!
Our newest features include KNN Join, GeoStats and DataFrame-based readers. Learn more about the latest release in our most recent office hour.
You can can access the presentation slides here and the kNN Join slides here. Be sure to save the date for the next office hour to stay up-to-date with the latest releases!
Spatial data should be treated as a first class citizen. Here’s an overview of Apache Sedona and some of its common use cases. Learn more.
A machine learning and statistical toolbox for WherobotsAI and Apache Sedona users. Learn about its use cases and what challenges it helps solve. Read more.
Ideal for developers, data scientists and engineers, this hands-on guide provides practical solutions for challenges in working with various types of geospatial data. Get access here.
Hear from an amazing lineup of speakers from the Google Maps and Google Earth teams, former ESRI and Wherobots. Topics include kNN Join, the origins of GIS Day, the history and future of cloud-native geospatial technology, and current work within the open source community. More details here.
Learn>Learn how WherobotsAI Raster Inference enables data platform and science teams to analyze our planet with satellite imagery faster, more reliably, and with a zero carbon footprint—using SQL and Python. This fully managed, high-performance, carbon-neutral planetary-scale computer vision solution makes AI/ML on satellite imagery accessible to most developers and data scientists.
We host monthly office hours as a way to engage with the community, share the latest updates and releases, along with future plans. If you’re working on something exciting with Apache Sedona, we’d love to hear about it. Save the date for the next office hour.
How We Delivered “Fields of The World” with RasterFlow: A Planetary-Scale GeoAI Pipeline
See how we used RasterFlow to run a 100TB+ global GeoAI pipeline, from feature mosaics to predictions and vectors, with reproducible workflows.
How well does SAM3 detect building footprints? Let’s ask the Wherobots Spatial AI 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 […]
Wherobots MCP Server: Building GEOINT Spatial Pipelines with AI Agents
I built three national-security GEOINT use cases on the Wherobots stack in days instead of weeks. A Critical Infrastructure Vulnerability (CIV) pipeline with two regional variants, plus a border-corridor analysis on real transportation segments. The Wherobots geospatial MCP server is what made that timeline possible. Most of the work in standing up a credible use […]
Change Detection Using AlphaEarth Foundations (Part 2)
Continue exploring how Alpha Earth Embeddings reveal change over time using scores.
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