EXPERT OPINION / PERSPECTIVE — We’re at present witnessing a mobilization of technical ambition paying homage to the Manhattan Undertaking, a realization that knowledge and compute are the brand new defining components of nationwide energy. I’m deeply energized by latest daring strikes in Washington, particularly the White Home’s launch of the “Genesis Mission” this previous November—an initiative designed to federate huge federal scientific datasets for built-in AI coaching—alongside the real-world deployment of GenAI.mil.
But, once I take a look at the speed of the business sector—from OpenAI launching its devoted Science division and NVIDIA trying to simulate the planet with Earth-2, to Google DeepMind aggressively crossing their AI breakthroughs into the geospatial area—it turns into clear that we’re nonetheless aiming too low. These initiatives should not simply modeling knowledge; they’re trying to mannequin actuality itself. American technical management is paramount, however that management is meaningless if it’s not ruthlessly and instantly utilized to our nationwide safety framework. We should take these huge, reality-simulating ideas and focus them particularly on the GEOINT mission.
An ideal instance of that is that earlier this yr, in July 2025, the geospatial world shifted. Google DeepMind launched the AlphaEarth Foundations (AEF) mannequin, and thru the onerous work of the Taylor Geospatial Engine (TGE) and the open-source neighborhood, these vector embeddings are actually publicly accessible on Supply Cooperative.
From Google
The joy is justified. AlphaEarth is a leap ahead as a result of it provides pixel-level embeddings fairly than the usual patch-level strategy. It doesn’t simply let you know “this 256×256 sq. incorporates a metropolis”; it tells you “this particular pixel is a part of a constructing, and it is aware of its neighbors.”
However as I take a look at this achievement from the attitude of nationwide safety, I see one thing else. I see a proof of idea for a functionality that the USA is uniquely positioned to construct—and should construct—to take care of resolution benefit.
Google has the web’s knowledge. However the intelligence neighborhood holds essentially the most numerous, multi-physics, and temporally deep repository of the Earth in human historical past.
It’s time for the USA to suggest and execute a Nationwide Geospatial-Intelligence Embedding Mannequin (NGEM).
Join the Cyber Initiatives Group Sunday publication, delivering expert-level insights on the cyber and tech tales of the day – on to your inbox. Join the CIG publication as we speak.
The Proposal: Past RGB
The AlphaEarth mannequin is spectacular, however it’s restricted by its coaching knowledge—primarily business optical imagery. Within the nationwide safety area, an optical picture is simply the tip of the spear. We do not simply see with gentle; we see with physics.
I’m proposing that we prepare a large, pixel-level basis mannequin that ingests all of its holdings. We aren’t speaking about simply throwing extra Sentinel-2 knowledge at a GPU. We’re speaking a couple of mannequin that generates embeddings from a unified ingest of:
- Multi-INT Imagery: Electro-optical (EO), Artificial Aperture Radar (SAR), Infrared/Thermal, Multispectral, and Hyperspectral.
- Vector Knowledge: The huge shops of Basis GEOINT (FG)—roads, borders, elevation meshes.
- The Vital Lacking Modality: Textual content. We should embed the tens of millions of intelligence stories, analyst notes, and completed intelligence merchandise ever written.
The Strategy: “The Unified Latent House”
The strategy would mirror the AlphaEarth structure—producing 64-dimensional (or larger) vectors for each coordinate on Earth—however with a large improve in complexity and utility.
In AlphaEarth, a pixel’s embedding vector encodes “visible similarity.” In an NGA NGEM, the embedding would encode phenomenological and semantic fact.
We might prepare the mannequin to map totally different modalities into the identical “latent house.”
- If a SAR picture exhibits a T-72 tank (via radar returns), and an EO picture exhibits a T-72 tank (via visible pixels), and a textual content report describes a “T-72 tank,” they need to all map to almost the identical mathematical vector.
- The mannequin turns into the common translator. It would not matter if the enter is a paragraph of textual content or a thermal signature; the output is a standardized mathematical illustration of the article.
The Outcomes: What Does This Give Us?
If we obtain this, we transfer past “pc imaginative and prescient” into “machine understanding.”
1. The “SAM Web site” Dimension Within the AlphaEarth evaluation, researchers discovered a “dimension 27” that unintentionally specialised in detecting airports. It was a serendipitous discovery of the mannequin’s inside logic. If we prepare NSEM on NGA’s holdings, we gained’t simply discover an airport dimension. We’ll seemingly discover dimensions that correspond to particular nationwide safety targets.
- Dimension 14 would possibly gentle up just for Floor-to-Air Missile (SAM) websites, no matter whether or not they’re camouflaged in optical imagery, as a result of the thermal and SAR layers give them away.
- Dimension 42 would possibly observe “maritime logistics exercise,” integrating port vectors with ship signatures.
2. Cross-Modal Search (Textual content-to-Pixel) At present, if an analyst needs to seek out “all airfields with prolonged runways within the Pacific,” they must depend on tagged metadata or run a selected pc imaginative and prescient classifier. With a multi-modal embedding mannequin, the analyst might merely sort a question from a report: “Suspected development of hardened plane shelters close to distinct ridge line.” As a result of we embedded the textual content of tens of millions of previous stories alongside the imagery, the mannequin understands the semantic vector of that phrase. It could actually then scan the whole globe’s pixel embeddings to seek out the mathematical match—immediately highlighting the situation, even when no human has ever tagged it.
3. Vector-Primarily based Change Detection AlphaEarth confirmed us that subtracting vectors from 2018 and 2024 reveals development. For the intelligence neighborhood, this turns into Automated Indications & Warning (I&W). As a result of the embeddings are spatially conscious and pixel-dense, we will detect refined shifts within the operate of a facility, not simply its footprint. A manufacturing unit that all of the sudden begins emitting warmth (thermal layer) or exhibiting new materials stockpiles (hyperspectral layer) will produce a large shift in its vector embedding, triggering an alert lengthy earlier than a human analyst notices the visible change.
The Cipher Temporary brings expert-level context to nationwide and world safety tales. It’s by no means been extra essential to know what’s taking place on the earth. Improve your entry to unique content material by changing into a subscriber.
The Intelligence Use Instances
- Automated Order of Battle: Immediately producing dynamic maps of army tools by querying the embedding house for particular signatures (e.g., “Present me all vectors matching a cellular radar unit”).
- Underground Facility Detection: By combining vector terrain knowledge, gravity/magnetic anomaly knowledge, and hyperspectral floor disturbances right into a single embedding, the mannequin might “see” what’s hidden.
- Sample of Life Evaluation: Because the mannequin is spatiotemporal (like AlphaEarth), it learns the “heartbeat” of a location. Deviations—like a port going silent or a sudden surge in RF exercise—grow to be mathematical anomalies that scream for consideration.
Conclusion
Google and the open-source neighborhood have given us the blueprint with AlphaEarth. They proved that pixel-level, spatiotemporal embeddings are the superior option to mannequin our altering planet.
However the mission requires greater than business knowledge. It requires the fusion of each sensor and each secret. By constructing this multi-modal embedding mannequin—fusion on the pixel stage—we will cease searching for needles in haystacks and begin utilizing a magnet.
That is the way forward for GEOINT. Now we have the info. Now we have the mission. It’s time to construct the mannequin.
Comply with Mark Munsell on LinkedIn.
The Cipher Temporary is dedicated to publishing a variety of views on nationwide safety points submitted by deeply skilled nationwide safety professionals.
Opinions expressed are these of the writer and don’t signify the views or opinions of The Cipher Temporary.
Have a perspective to share based mostly in your expertise within the nationwide safety subject? Ship it to Editor@thecipherbrief.com for publication consideration.
Learn extra expert-driven nationwide safety insights, perspective and evaluation in The Cipher Temporary, as a result of nationwide safety is everybody’s enterprise.
