According to @DeepLearningAI, Google released AlphaEarth Foundations, a set of time embeddings representing every 10 meter square of Earth from 2017 to 2024, trained on optical, radar, and thermal satellite data and aligned with geotagged text to help track climate, land use, vegetation, water, and more, and the dataset is free to download under a CC BY 4.0 license with Google offering grants to teams that use it, source: @DeepLearningAI. For trading and risk analytics, the source specifies global, multi modal coverage and time consistency that support systematic monitoring of climate, land use, vegetation, and water signals across 2017 to 2024 that can be directly derived from the released embeddings, source: @DeepLearningAI.

According to Demis Hassabis, AlphaEarth Foundations is a new AI model that integrates petabytes of satellite data into a unified digital representation of Earth, delivering nearly real-time, high-precision planetary insights. This innovation is poised to provide traders and analysts with unprecedented access to environmental and food security data, potentially impacting agricultural commodity markets, supply chain risk assessments, and the broader crypto market by enabling data-driven trading strategies based on global resource trends (source: Demis Hassabis).

According to Google DeepMind, their newly launched AlphaEarth Foundations AI model is capable of mapping the planet in unprecedented detail, enabling scientists to monitor deforestation and crop health much more efficiently. This development introduces high-frequency, accurate environmental datasets that could significantly enhance ESG (Environmental, Social, and Governance) data available to blockchain projects and crypto investors, supporting data-driven tokenization of environmental assets and increased transparency for sustainability-focused cryptocurrencies. Source: Google DeepMind

According to Google DeepMind, AlphaEarth Foundations provides 24% lower error rates compared to other AI-based and traditional methods. Additionally, its compact observation summaries are 16 times more storage-efficient, significantly reducing compute and storage requirements for users. These advancements in AI efficiency may influence the demand for decentralized compute and storage solutions, potentially impacting related crypto projects and tokens focused on AI infrastructure and data storage (source: Google DeepMind).