Master AtlasAI with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Explore the key features that make AtlasAI powerful for geospatial ai workflows.
Atlas AI is a Geospatial AI platform headquartered in Palo Alto, California, founded in 2018 by Stanford professor Marshall Burke, Abe Tarapani, David Lobell, and Vivek Subramanian. The company emerged from Stanford research on using satellite imagery and machine learning to measure economic activity in data-sparse regions. It now serves enterprise customers in consumer goods, logistics, energy, manufacturing, and humanitarian sectors. The platform's mission is to shine a light on future opportunity and risk amid a rapidly changing planet.
Atlas AI does not publish pricing on its website. Engagements are sold as custom enterprise contracts, with scope and price determined after a discovery conversation with the sales team. Based on our analysis of similar enterprise GeoAI platforms in our directory, contracts of this nature typically start in the five-to-six-figure range annually, depending on data coverage, model usage, and integration scope. Prospective customers should expect a sales-led process rather than a self-serve signup.
Aperture® Pulse is Atlas AI's recently launched product designed to surface change in global markets faster than ever before. It builds on the broader Aperture line by combining satellite-derived signals with Atlas AI's socio-demographic models to detect shifts in population, economic activity, and infrastructure. The product is positioned for users who need timely change detection across geographies, including conflict-prone regions and emerging markets. It is available as part of Atlas AI's enterprise platform.
Atlas AI is best for enterprises that need forecasted, ML-derived socio-demographic and economic indicators rather than traditional GIS mapping or visualization. ArcGIS and Google Earth Engine are far more flexible for general spatial analysis, cartography, and raw imagery processing, but they require teams to build their own forecasting and feature-engineering pipelines. Atlas AI ships pre-built models and an analysis-ready feature store, which shortens time-to-insight for demand forecasting, site selection, and market segmentation. Choose Atlas AI when the goal is business outcomes from human-scale insights rather than spatial tooling.
Atlas AI provides an Enterprise Developer Toolkit that includes connectors, interfaces, and handlers built to plug GeoAI features into existing ML pipelines. Geo-referenced analysis-ready data (ARD) is delivered through the Geospatial Feature Store, where features can be pulled into standard ML workflows. The GeoAI Model Library exposes production-scale analytical models that data scientists can call as components rather than rebuilding from scratch. This makes the platform deployable inside existing cloud and notebook environments without forcing teams onto a proprietary UI.
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Tutorial updated March 2026