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Beyond the Hype: Element 84’s Pragmatic Vision for AI in Engineering
At Element 84, we’ve always been focused on solving our clients’ most complex geospatial problems with high-quality, reliable, and scalable software. We’re excited about AI’s potential to accelerate development and allow our engineers to focus their creative energy on core problem-solving. To achieve that without sacrificing our quality and reliability, our approach is centered around…
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Beyond the Hype: Element 84’s Pragmatic Vision for AI in Engineering
At Element 84, we’ve always been focused on solving our clients’ most complex geospatial problems with high-quality, reliable, and scalable software. We’re excited about AI’s potential to accelerate development and allow our engineers to focus their creative energy on core problem-solving. To achieve that without sacrificing our quality and reliability, our approach is centered around…
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Exploring AlphaEarth Embeddings
Earlier this year, in July 2025, Google DeepMind contributed to the conversation on Earth Observation (EO) foundation models and vector embeddings by releasing a dataset of EO vector embeddings generated by their new AlphaEarth Foundations (AEF) model. Originally released only inside Google Earth Engine, these embeddings were finally made publicly and freely available on Source…
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Metadata makes the data format: metadata storage and representation across array formats
In this continuation of our recent raster data format blog series we discuss metadata: how do COG and Zarr represent metadata and how can geospatial coordinate metadata be represented across different formats? Where should metadata be stored? and more!
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The 2025 Geospatial Tech Radar: Agentic AI, EO Embeddings, and Cloud-Native Maturity
We analyze trends and highlights from the third iteration of our Geospatial Tech Radar including Agentic AI and MCP, EO Foundation Models and Embeddings, and the solidification of Cloud-Native Geospatial.
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STAC: A Retrospective, Part 2 (Why STAC was Successful)
In Part 1 of this series we covered the origin story of STAC, exploring the history and the initial sprints that created the spec. Now, it’s time to cover the how and the why of its success.
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Designing risk visualizations: why appropriate complexity matters
In this blog I detail some case studies that have been top-of-mind recently to demonstrate the importance of appropriate complexity, and how it can contribute to meaningful action when applied with intention, particularly in the context of risk analysis.
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Mobilizing Metadata; using STAC to Manage Risk in the Reinsurance Industry
Data doesn’t have any value unless you can use it… and trust it. Users need to quickly access data and know where it came from. This is especially true with the integration of AI, making quick access more achievable but data provenance more opaque. This is where STAC comes in. The reinsurance industry uses climate…
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Beyond the default: a modern guide to raster compression
We discuss community concerns surrounding raster compression and key metrics for evaluating compression effectiveness.
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Introducing Natural Language Geocoding v0.1.0: An LLM-Enabled Geocoding Database
The landscape of geospatial AI is rapidly evolving. Many organizations are building LLM-powered solutions that tackle complex geospatial problems and answer sophisticated questions about our planet. These agentic approaches allow LLMs to autonomously select from toolsets that include geospatial tools, Earth Observation (EO) catalogs, and EO data processing capabilities. The result? Systems that can process…
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Chunks and Chunkability: An Origin Story
If you accept that we are living through the Tyranny of the Chunk, then you might be wondering how we got to this place. Who created the first chunks and what was their motivation? As we explore the history of different data formats we’ll see recurring patterns of thinking around how much information to consolidate…
