EarthOne is an enterprise‑grade geospatial analytics and machine learning platform designed to transform Earth observation data into operational intelligence. It provides a fully hosted environment where users can access large geospatial datasets, develop analytical and AI models, and deploy them at scale without managing underlying infrastructure. The platform bridges research and production by enabling rapid prototyping of geospatial models and seamless transition into automated, large‑scale operational workflows. EarthOne supports real‑time and batch processing, allowing organizations to move beyond static analysis toward continuous monitoring and decision support. EarthOne is built to handle petabyte‑scale geospatial data and integrates data access, computing, analytics, and workflow orchestration in a single environment. It is designed for data scientists, engineers, and geospatial teams that require scalable, repeatable, and production‑ready geospatial intelligence.

Key Features

Centralized Geospatial Data Catalog

Manages large and complex datasets.

• Supports raster, vector, and time‑series data
• Connects internal and external data sources

Hosted Analytics and Development Environment

Enables rapid model development.

• Integrated Python programming environment
• Supports iterative prototyping and testing

AI and Machine Learning Enablement

Designed for geospatial modeling.

• Supports training and deployment of AI models
• Optimized for large‑scale spatial computation

Workflow Orchestration and Automation

Operationalizes analytics.

• Event‑driven processing pipelines
• Automated ingestion, transformation, and analysis

Scalable Cloud Compute

Handles high‑volume processing.

• Dynamically allocated compute resources
• Supports parallel processing at scale

Monitoring and Alerting

Enables real‑time awareness.

• Continuous monitoring of incoming data
• Configurable alerts based on analytical outputs

Benefits

Accelerates Geospatial Innovation

Reduces time from idea to deployment.

• Faster prototyping cycles
• Direct path from research to operations

Eliminates Infrastructure Complexity

Simplifies platform management.

• Fully hosted SaaS delivery
• No need to manage compute or storage

Enables Real‑Time Decision Making

Moves beyond static analysis.

• Near‑real‑time processing and alerts
• Continuous operational monitoring

Scales with Data and Demand

Supports enterprise‑level workloads.

• Handles petabyte‑scale datasets
• Suitable for global‑scale analytics

Improves Collaboration

Centralizes tools and data.

• Shared environment for teams
• Consistent workflows and outputs