AGENTIC-SYSTEMSOctober 24, 20251 min read21 views

AlphaEarth Foundations helps map our planet in unprecedented detail

New AI model integrates petabytes of Earth observation data to generate a unified data representation that revolutionizes global mapping and monitoring.

New AI model integrates petabytes of Earth observation data to generate a unified data representation that revolutionizes global mapping and monitoring

Every day, satellites capture information-rich images and measurements, providing scientists and experts with a nearly real-time view of our planet. While this data has been incredibly impactful, its complexity, multimodality and refresh rate creates a new challenge: connecting disparate datasets and making use of them all effectively.

Today, we’re introducing AlphaEarth Foundations, an artificial intelligence (AI) model that functions like a virtual satellite. It accurately and efficiently characterizes the planet’s entire terrestrial land and coastal waters by integrating huge amounts of Earth observation data into a unified digital representation, or "embedding," that computer systems can easily process. This allows the model to provide scientists with a more complete and consistent picture of our planet's evolution, helping them make more informed decisions on critical issues like food security, deforestation, urban expansion, and water resources.

To accelerate research and unlock use cases, we are now releasing a collection of AlphaEarth Foundations’ annual embeddings as the Satellite Embedding dataset in Google Earth Engine. Over the past year, we’ve been working with more than 50 organizations to test this dataset on their real-world applications.

Our partners are already seeing significant benefits, using the data to better classify unmapped ecosystems, understand agricultural and environmental changes, and greatly increase the accuracy and speed of their mapping work. In this blog, we are excited to highlight some of their feedback and showcase the tangible impact of this new technology.

Source: Google DeepMind Blog

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