Machine Learning for Earth Observation Market Map
Meet the 100+ organizations that focus on machine learning applications with satellite data
Building geospatial machine learning applications involve many dependable moving parts, from accessing Earth observation (EO) data, labeling imagery, and generating training data to creating and developing models and running analytics. A growing list of organizations from various sectors are providing solutions and services to advance these applications.
Who can help you build machine learning applications, identify patterns from your data, or run your crowdsourcing campaign? What organizations are providing software or a platform that you can utilize to develop your machine learning model?
To help you navigate these questions, we curated a list of organizations focusing on different aspects of machine learning with a satellite data pipeline. The list of organizations are divided into two groups — Commercial and Non-Commercial — and five categories:
- Data Analysis and Services — Organizations that build machine learning models and analytical solutions for various geospatial applications using EO.
- Analytics Platforms — Organizations that provide software or a platform to process EO data and build customized applications.
- Labeling Platforms — Organizations that provide a solution for generating labels on satellite imagery and creating training data catalogs.
- Competition Platforms — Organizations that help you run challenges on your training dataset to outsource modeling solutions.
- Data Access and Storage — Organizations that provide access to satellite imagery and geospatial training data to facilitate machine learning analytics pipelines.
What organizations are we missing?
Please follow this link to submit the names of organizations we might have missed: http://bit.ly/MissingML4EOOrgs.
Additional notes on entries:
- While openEO itself is indeed an open-source standard and non-commercial, many of the actual platforms that implement the standard will eventually be commercial.