Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders.<\/em> Subscribe Now<\/em><\/p>\n\n\n\n Google DeepMind announced today a breakthrough artificial intelligence system that transforms how organizations analyze Earth\u2019s surface, potentially revolutionizing environmental monitoring and resource management for governments, conservation groups, and businesses worldwide.<\/p>\n\n\n\n The system, called AlphaEarth Foundations, addresses a critical challenge that has plagued Earth observation for decades: making sense of the overwhelming flood of satellite data streaming down from space. Every day, satellites capture terabytes of images and measurements, but connecting these disparate datasets into actionable intelligence has remained frustratingly difficult.<\/p>\n\n\n\n \u201cAlphaEarth Foundations functions like a virtual satellite,\u201d the research team writes in their paper. \u201cIt accurately and efficiently characterizes the planet\u2019s entire terrestrial land and coastal waters by integrating huge amounts of Earth observation data into a unified digital representation.\u201d<\/p>\n\n\n\n The AI system reduces error rates by approximately 23.9% compared to existing approaches while requiring 16 times less storage space than other AI systems. This combination of accuracy and efficiency could dramatically lower the cost of planetary-scale environmental analysis.<\/p>\n\n\n\n The AI Impact Series Returns to San Francisco – August 5<\/strong><\/p>\n\n\n\n The next phase of AI is here – are you ready? Join leaders from Block, GSK, and SAP for an exclusive look at how autonomous agents are reshaping enterprise workflows – from real-time decision-making to end-to-end automation.<\/p>\n\n\n\n Secure your spot now – space is limited: https:\/\/bit.ly\/3GuuPLF<\/p>\n\n\n\n The core innovation lies in how AlphaEarth Foundations processes information. Rather than treating each satellite image as a separate piece of data, the system creates what researchers call \u201cembedding fields\u201d \u2014 highly compressed digital summaries that capture the essential characteristics of Earth\u2019s surface in 10-meter squares.<\/p>\n\n\n\n \u201cThe system\u2019s key innovation is its ability to create a highly compact summary for each square,\u201d the research team explains. \u201cThese summaries require 16 times less storage space than those produced by other AI systems that we tested and dramatically reduces the cost of planetary-scale analysis.\u201d<\/p>\n\n\n\n This compression doesn\u2019t sacrifice detail. The system maintains what the researchers describe as \u201csharp, 10\u00d710 meter\u201d precision while tracking changes over time. For context, that resolution allows organizations to monitor individual city blocks, small agricultural fields, or patches of forest \u2014 critical for applications ranging from urban planning to conservation.<\/p>\n\n\n\n More than 50 organizations have been testing the system over the past year, with early results suggesting transformative potential across multiple sectors.<\/p>\n\n\n\n In Brazil, MapBiomas uses the technology to understand agricultural and environmental changes across the country, including within the Amazon rainforest. \u201cThe Satellite Embedding dataset can transform the way our team works,\u201d Tasso Azevedo, founder of MapBiomas, said in a statement. \u201cWe now have new options to make maps that are more accurate, precise and fast to produce \u2014 something we would have never been able to do before.\u201d<\/p>\n\n\n\n The Global Ecosystems Atlas initiative employs the system to create what it calls the first comprehensive resource for mapping the world\u2019s ecosystems. The project helps countries classify unmapped regions into categories like coastal shrublands and hyper-arid deserts \u2014 crucial information for conservation planning.<\/p>\n\n\n\n \u201cThe Satellite Embedding dataset is revolutionizing our work by helping countries map uncharted ecosystems \u2014 this is crucial for pinpointing where to focus their conservation efforts,\u201d said Nick Murray, Director of the James Cook University Global Ecology Lab and Global Science Lead of Global Ecosystems Atlas.<\/p>\n\n\n\n The research paper reveals sophisticated engineering behind these capabilities. AlphaEarth Foundations processes data from multiple sources \u2014 optical satellite images, radar, 3D laser mapping, climate simulations, and more \u2014 weaving them together into a coherent picture of Earth\u2019s surface.<\/p>\n\n\n\n What sets the system apart technically is its handling of time. \u201cTo the best of our knowledge, AEF is the first EO featurization approach to support continuous time,\u201d the researchers note. This means the system can create accurate maps for any specific date range, even interpolating between observations or extrapolating into periods with no direct satellite coverage.<\/p>\n\n\n\n The model architecture, dubbed \u201cSpace Time Precision\u201d or STP, simultaneously maintains highly localized representations while modeling long-distance relationships through time and space. This allows it to overcome common challenges like cloud cover that often obscures satellite imagery in tropical regions.<\/p>\n\n\n\n For technical decision-makers in enterprise and government, AlphaEarth Foundations could fundamentally change how organizations approach geospatial intelligence.<\/p>\n\n\n\n The system excels particularly in \u201csparse data regimes\u201d \u2014 situations where ground-truth information is limited. This addresses a fundamental challenge in Earth observation: while satellites provide global coverage, on-the-ground verification remains expensive and logistically challenging.<\/p>\n\n\n\n \u201cHigh-quality maps depend on high-quality labeled data, yet when working at global scales, a balance must be struck between measurement precision and spatial coverage,\u201d the research paper notes. AlphaEarth Foundations\u2019 ability to extrapolate accurately from limited ground observations could dramatically reduce the cost of creating detailed maps for large areas.<\/p>\n\n\n\n The research demonstrates strong performance across diverse applications, from crop type classification to estimating evapotranspiration rates. In one particularly challenging test involving evapotranspiration \u2014 the process by which water transfers from land to atmosphere \u2014 AlphaEarth Foundations achieved an R\u00b2 value of 0.58, while all other methods tested produced negative values, indicating they performed worse than simply guessing the average.<\/p>\n\n\n\n The announcement places Google at the forefront of what the company calls \u201cGoogle Earth AI\u201d \u2014 a collection of geospatial models designed to tackle planetary challenges. This includes weather predictions, flood forecasting, and wildfire detection systems that already power features used by millions in Google Search and Maps.<\/p>\n\n\n\n \u201cWe\u2019ve spent years building powerful AI models to solve real-world problems,\u201d write Yossi Matias, VP & GM of Google Research, and Chris Phillips, VP & GM of Geo, in an accompanying blog post published this morning. \u201cThese models already power features used by millions, like flood and wildfire alerts in Search and Maps; they also provide actionable insights through Google Earth, Google Maps Platform and Google Cloud Platform.\u201d<\/p>\n\n\n\n The release includes the Satellite Embedding dataset, described as \u201cone of the largest of its kind with over 1.4 trillion embedding footprints per year,\u201d available through Google Earth Engine. This dataset covers annual snapshots from 2017 through 2024, providing historical context for tracking environmental changes.<\/p>\n\n\n\n Google emphasizes that the system operates at a resolution designed for environmental monitoring rather than individual tracking. \u201cThe dataset cannot capture individual objects, people, or faces, and is a representation of publicly available data sources, such as meteorological satellites,\u201d the company clarifies.<\/p>\n\n\n\n The 10-meter resolution, while precise enough for most environmental applications, intentionally limits the ability to identify individual structures or activities \u2014 a design choice that balances utility with privacy protection.<\/p>\n\n\n\n The availability of AlphaEarth Foundations through Google Earth Engine could democratize access to sophisticated Earth observation capabilities. Previously, creating detailed maps of large areas required significant computational resources and expertise. Now, organizations can leverage pre-computed embeddings to generate custom maps rapidly.<\/p>\n\n\n\n \u201cThis breakthrough enables scientists to do something that was impossible until now: create detailed, consistent maps of our world, on-demand,\u201d the research team writes. \u201cWhether they are monitoring crop health, tracking deforestation, or observing new construction, they no longer have to rely on a single satellite passing overhead.\u201d<\/p>\n\n\n\n For enterprises involved in supply chain monitoring, agricultural production, urban planning, or environmental compliance, the technology offers new possibilities for data-driven decision-making. The ability to track changes at 10-meter resolution globally, with annual updates, provides a foundation for applications ranging from verifying sustainable sourcing claims to optimizing agricultural yields.<\/p>\n\n\n\n The Satellite Embedding dataset is available now through Google Earth Engine, with AlphaEarth Foundations continuing development as part of Google\u2019s broader Earth AI initiative. As one researcher noted during the press briefing, the question facing organizations isn\u2019t whether they need planetary-scale intelligence anymore \u2014 it\u2019s whether they can afford to operate without it.<\/p>\n Daily insights on business use cases with VB Daily<\/strong><\/p>\n If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI.<\/p>\n \n\t\t\t\t\tThanks for subscribing. Check out more VB newsletters here.\n\t\t\t\t<\/p>\n An error occured.<\/p>\n<\/p><\/div>\n
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\n<\/div>How the AI compresses petabytes of satellite data into manageable intelligence<\/h2>\n\n\n\n
Brazilian researchers use the system to track Amazon deforestation in near real-time<\/h2>\n\n\n\n
The system solves satellite imagery\u2019s biggest problem: clouds and missing data<\/h2>\n\n\n\n
Why enterprises can now map vast areas without expensive ground surveys<\/h2>\n\n\n\n
Google positions Earth monitoring AI alongside its weather and wildfire systems<\/h2>\n\n\n\n
The 10-meter resolution protects privacy while enabling environmental monitoring<\/h2>\n\n\n\n
A new era of planetary intelligence arrives through Google Earth Engine<\/h2>\n\n\n\n
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