The basics of Alphaearth helps to map our planet in unprecedented details

The new AI model integrates petabytes of land observation data to generate a unified data representation that revolutionizes global mapping and monitoring

Every day, satellites record paintings and information rich in information, providing scientists and experts in real time the sight of our planet. Although these data were extremely influential, their complexity, multimodality and refresh rate creates a new challenge: combining different data sets and effective use of them.

Today we are introducing the foundations of Alphaarius, a model of artificial intelligence (AI), which acts like a virtual satellite. It is accurately and effectively characterized by the entire earth and coastal waters of planets, integrating huge amounts of data observation data into a unified digital representation or “deposition,“Computer systems can easily process. This allows the model to provide scientists with a more complete and coherent picture of the evolution of our planet, helping them make more conscious decisions on critical issues, such as food safety, desertion, urban expansion and water resources.

To accelerate research and unlock cases of use, we now release a set of annual embedding of Alphaearth foundations as Satellite data set IN Google Earth Engine. Over the past year, we have worked with over 50 organizations to test this set of data in their applications in the real world.

Our partners already see significant benefits, use data for better classification of unforgettable ecosystems, understanding agricultural and environmental changes, and significantly increase the accuracy and speed of their mapping. On this blog we are glad that we can emphasize some of their opinions and present the tangible impact of this new technology.

How the foundations of alphaaria work

Alphaearth foundations provide a new lens for understanding our planet by solving two main challenges: overloading data and inconsistent information.

First of all, it combines the amounts of information from dozens of different public sources – optical satellite images, radar, 3D laser mapping, climate simulation and others. He makes all this information to analyze the world land and coastal waters in acute squares with a length of 10 x 10 meters, allowing it to track changes with extraordinary precision over time.

Secondly, it makes this data practical. The key system innovation is its ability to create a highly compact summary for each square. These summaries require 16 times less storage space than those produced by other AI systems that we tested and radically reduces the cost of planetary analysis.

This breakthrough allows scientists to do something that has been impossible so far: to create detailed, coherent maps of our world, on demand. Regardless of whether they monitor the health of crops, follow the desecration or observe a new structure, they no longer have to rely on one satellite ride. They now have a new type of generational data basics.

To make sure that the foundations of Alphaearth were ready for use in the real world, we strictly tested its performance. Compared to both traditional methods and other artificial intelligence mapping systems, the foundations of Alphaari were consistently the most accurate. He stood out in a wide range of tasks at various periods, including in identification of land use and estimating surface properties. Most importantly, it achieved in the scripts when the labels were rare. On average, Alphaari foundations had a 24% lower error level than we tested models, showing its excellent learning performance. Learn more in ours paper.

Generating custom maps using a set of satellite deposition data

Powered by alphaari foundations, Satellite data set In Google Earth Engine is one of the largest of its kind, with over 1.4 trillion of prisoning feet annually. This collection of annual prisoners is already used by organizations around the world, including the United Nations Organization of food and agricultureIN Harvard ForestIN Group on EarthIN MapbiomasIN Oregon State UniversityThe Spatial IT group AND Stanford UniversityTo create powerful non -standard maps that drive real observations.

For example, Global ecosystems atlasAn initiative aimed at creating the first comprehensive resource of mapping and monitoring of global ecosystems, is to use this set of data to help countries in the classification of unforgettable ecosystems for categories such as categories Coast AND Hiper-ARID desert. This first resource of this kind plays a key role in helping countries in a better priority of protection areas, optimizing the efforts of restoring and combating the loss of biological diversity.

In Brazil, Mapbiomas He tests a set of data to understand agricultural and environmental changes in the whole country deeper. This type of map informs about the protection strategies and initiatives of sustainable development in critical ecosystems such as Amazon Rainforest.

As Tasso Azevedo said, the founder of Mapbiomas: “Satellite data set can transform the way our team works – we now have new maps creating options that are more accurate, precise and fast for production – something that we could never do before.”

Read more about a set of satellite data and see tutorials in Google Earth Engine blog .

Strengthening others with AI

The basics of alphaaria are a significant step forward in understanding the state and dynamics of our changing planet. We are currently using alphaarithic foundations to generate annual prisoners and we think that they can be even more useful in the future in combination with general LLM agents, such as Gemini. We are still investigating the best ways to apply opportunities based on times under our model under Google Earth AIOur collection of geopolter models and data sets to help satisfy the planet's most critical needs.