AI-powered pixels: Introducing Google’s Satellite Embedding dataset
We’re introducing a new way to analyze the planet. Google’s Satellite Embedding dataset uses the power of AI to pack a year’s worth of multi-source satellite data into every single 10-meter pixel, enabling faster and more powerful geospatial analysis. Welcome to the future of deep learning in Earth Engine.
Fifteen years ago, we launched Earth Engine with a mission to provide widespread access to Earth observation imagery and geospatial data. As we’ve added petabytes of publicly available data to the Earth Engine Data Catalog, this ambitious goal has brought a new challenge: how can users effectively leverage ever-growing image archives and a multitude of inputs and algorithms to address the world’s most pressing environmental issues? Answer: The power of AI!
Today, we are excited to introduce our new Satellite Embedding dataset produced in partnership with Google DeepMind. This first-of-its-kind dataset was generated using AlphaEarth Foundations, Google DeepMind’s new geospatial AI model that assimilates observations across diverse sources of geospatial information, including optical and thermal imagery from Sentinel-2 and Landsat satellites, radar data that can see through clouds, 3D measurements of surface properties, global elevation models, climate information, gravity fields, and descriptive text. Unlike traditional deep learning models that require users to fine-tune weights and run their own inference on clusters of high-end computers, AlphaEarth Foundations was designed to produce information-rich, 64-dimensional geospatial “embeddings” that are suitable for use with Earth Engine’s built-in machine learning classifiers and other pixel-based analysis.
