Leveraging Google’s AlphaEarth Foundations for AI Data Center Intelligence
Introduction: A New Lens on Historical Change
At RediMinds, we turn complex data into actionable strategic intelligence. Our exploration of Google’s AlphaEarth Foundations model is a prime example. This case study details how we used this cutting-edge geospatial AI to perform precise change detection in Troy, Michigan, demonstrating a methodology with direct applications for high-stakes decisions like AI data center site selection.
The Challenge: Moving Beyond Traditional Satellite Imagery
Traditional satellite imagery is often hindered by clouds, scan lines, and data gaps. For tasks requiring a clear, historical understanding of land use change, such as identifying sites with a history of stable development or available infrastructure, a more robust and analytical approach is needed. We needed to not just see the landscape, but quantify how it has transformed over time.
Our Solution: A Deep Dive into Satellite Embeddings
We leveraged the Satellite Embedding VI dataset, a global, analysis-ready collection of 64-dimensional geospatial embeddings produced by Google and Google DeepMind, available through Google Earth Engine (GEE).
The Technology: Instead of raw pixels, this model represents each 10m x 10m area as a compact vector that encapsulates approximately one year of multi-source data (Landsat, Sentinel-1/2, LiDAR). This approach is 23-24% more accurate than leading AI mapping models and requires 16x less storage than raw data.
Our Methodology: We executed a targeted analysis to detect changes in Troy, Michigan, between 2017 and 2023. The process was methodical:
Data Access & Selection: We accessed the GOOGLE/SATELLITE_EMBEDDING_VI/ANNUAL collection via the GEE JavaScript API and filtered it for the years 2017 and 2023.
Band Selection for Visualization: Since the 64 bands (A00-A63) are not natively interpretable, we selected three specific bands (A01, A16, and A09) to create an RGB composite. This combination, informed by heuristics suggesting early-to-mid bands capture optical and temporal patterns, effectively highlighted urban features.
Change Detection via Dot Product Similarity: The core of our analysis was computing a dot product similarity between the 2017 and 2023 embedding images. This involved multiplying the embeddings pixel-by-pixel and summing across all 64 bands.
Result:The output was a clear similarity map where brighter areas indicated greater change (lower similarity), such as new construction, and darker areas indicated stability (higher similarity).
Key Insights and Value Delivered
Our analysis generated a precise, quantifiable map of urban transformation in Troy. The visualizations for 2017 and 2023, alongside the similarity map, provided an unambiguous story of development.
More importantly, this project served as a critical proof-of-concept for our strategic vision. The ability to track urban expansion, land use shifts, and infrastructure development over time is directly transferable to the complex problem of AI data center site selection. This methodology allows us to:
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Identify areas with a history of development suitable for construction.
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Analyze proximity and growth of key infrastructure like power grids and transportation networks.
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Provide data-driven narratives that are more accurate and verifiable than those generated by LLMs alone, grounding strategic decisions in factual, geospatial evidence.
Conclusion: Building a Foundation for Intelligent Infrastructure
This exploration in Troy, Michigan, was more than an technical exercise; it was a foundational step in our R&D. It proves our capability to leverage leading-edge Geospatial AI to deliver deep, factual insights for infrastructure planning.
We are now applying this proven methodology to help clients identify optimal locations for AI data centers by analyzing critical factors like energy infrastructure, cooling water availability, and land stability. At RediMinds, we are committed to building the intelligent software stack that will accelerate the development of vital technological infrastructure.
Technology Stack: Google Earth Engine, Google & DeepMind’s AlphaEarth Foundations (Satellite Embedding VI) Dataset, JavaScript API.