Industries · Defense & security
Dense, high-stakes data for defense AI
SAR satellite imagery with 100+ ships per image, drone video tracked frame by frame, and tens of thousands of news articles tagged for intelligence — labeled precisely and delivered in your exact format.
Overview
Labeling radar, drone, and intelligence data that won't sit still
Defense and security AI is trained on data that looks nothing like a consumer photo set. Synthetic aperture radar (SAR) sees through cloud and darkness by bouncing radar off the surface, so its images read as grain and bright returns rather than familiar shapes. Drone video moves with the aircraft, and its perspective changes every second. Intelligence text arrives as tens of thousands of articles written by different people, for different audiences, in different styles. Each of these needs labels that are dense, precise, and consistent, because a model that misses a vessel in a crowded harbor or confuses two people in a report fails exactly where it’s needed.
What good looks like is specific. Boxes follow each object’s orientation instead of sitting square to the frame. Tracks stay attached to the same object as the camera drifts. Entities, relationships, and references in text are tagged the same way on the last article as on the first. And the finished set arrives in the exact format your training code reads, with every version recorded, so the program can show what changed and when.
MLtwist runs defense work as a pipeline, not just a labeling job. Imagery is prepared before anyone labels it, pre-labeling is tuned to the footage rather than taken off the shelf, and quality checks happen wherever they work best — including outside the labeling tool when a dense video overwhelms it. Labeling can be staffed by our vetted team, your own cleared reviewers, or both, with the same QA and audit trail either way. For more on how video programs run, see video and computer vision; for buying and oversight, see public sector.
Bring MLtwist in when the data is too dense, too unstable, or too sensitive for a generic labeling vendor, or when your own tooling stalls on volume. We’ve labeled SAR satellite images with more than 100 ships each, tracked objects frame by frame in drone SAR video, and tagged news text for intelligence analysis.
The problem
What makes defense & security data hard
Density
Some SAR satellite images hold more than 100 vessels, and some drone frames more than 100 objects. At that density, a small error in one box repeats across a whole sequence, and a missed object in a crowded scene is easy for a reviewer to miss too.
Motion
Drone footage shifts constantly with wind, altitude changes, and lateral drift, so perspective and scale change from one frame to the next. Every box needs adjusting frame by frame, and pre-labeling built for steady cameras can add work instead of saving it.
Strict pack-out
Defense models often expect a specific, non-generic output format. Anything else means your engineers write conversion code and re-check the results before training can start — time a program on a deadline doesn't have.
How it works
How a defense program runs
Defense data is usually dense, moving, or both. The work around the labeling tool matters as much as the tool.
- 01
Prepare the imagery
Large SAR tiles are split and resized into consistent, annotation-ready images, so labelers work at a usable scale. Long drone captures are cut into workable clips. The same preparation runs on every batch, so the next delivery matches the last.
- 02
Tune the pre-labeling
An AI model drafts boxes and tracks before people start. On drone SAR footage, default pre-labeling sometimes made correction slower, so MLtwist adapted it to the footage to cut manual rework. The goal is fewer corrections, not just more automatic boxes.
- 03
Label to the object
Annotators use AI-assisted tools for tilted bounding boxes that follow each ship's or vehicle's orientation as the viewpoint changes. For text, entities are tagged across 13 fields — including people, organizations, locations, geopolitical events, and weapons systems — with relations and coreference resolved.
- 04
Review as video
Automated checks clean misalignments and keep boxes consistent across frames. When annotation volume makes the labeling tool lag, labeled video is extracted, rebuilt in your format, and reviewed as smooth, playable video, where drift shows up that single frames hide.
- 05
Pack out
Datasets are delivered in your model's exact format, so they go into training without post-processing. Every annotation is version-controlled, so any delivery can be traced, repeated, or compared with the one before it.
Case study · Defense company
Tracking people, vehicles, and containers in drone SAR video
A defense company needed frame-level tracking of people, vehicles, and containers in synthetic aperture radar video captured by drones — tens of thousands of frames per video, sometimes more than 100 boxes per frame. Wind, altitude changes, and drift shifted the viewpoint constantly.
Default pre-labeling wasn't enough: on footage this unstable, it sometimes increased correction time. MLtwist adapted its AI-assisted pipeline to drone SAR data, annotators used tilted boxes that follow each object's orientation, and proprietary algorithms cleaned misalignments, removed noise, and kept boxes consistent across frames.
The sheer number of annotations made in-tool QA impractical, so MLtwist extracted the fully labeled videos, rebuilt them in the client's required format, and reviewed them as playable video. Box precision held up despite the drone's movement, the tuned pre-labeling reduced the correction workload, and the finished datasets went straight into the client's model training and operational systems.
- boxes per frame, tracked frame by frame
- 100+
What you get
Training data that drops into your pipeline
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Your exact format
Output is written directly in the non-generic format your model expects. Training starts on delivery, with no conversion step on your side.
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Version control
Every annotation is versioned for transparency and repeatability. You can see what changed between deliveries and reproduce any of them.
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Tracks checked as video
Reviewers watch labeled playback, not isolated frames. Drift, missed objects, and ID switches show up the way your model would meet them.
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Staffed to your rules
Our vetted team, your cleared reviewers, or both. The QA process and the record of who touched each file stay the same whichever you choose.
More work
Related programs
Defense company · Defense & security
How MLtwist Streamlined AI Data Processing for SAR-Based Ship Detection Models
Defense technology company · Defense & security
Defense Company Uses MLtwist for Large-Scale NLP on News Articles
FAQ
Common questions
Can you work with SAR data?
Yes. MLtwist has labeled thousands of SAR satellite images with tilted bounding boxes — sometimes more than 100 ships per image — and tracked people, vehicles, and containers in drone-captured SAR video. Large SAR tiles are split and resized before labeling so annotators work at a consistent scale.
Why tilted bounding boxes instead of standard ones?
A standard box sits square to the image, so a ship at an angle gets a box that is mostly water. Tilted boxes follow each object's orientation, which gives the model a truer outline. In MLtwist's SAR ship-detection work, that alignment improved vessel detection in training.
Can our cleared staff do the labeling?
Yes. Use our team, your own cleared reviewers working in MLtwist's tooling, or both — a common setup is our team for volume and your specialists for the hard cases. The QA process and the record of who touched each file stay the same.
Do you handle text intelligence data?
Yes. For one defense technology company, MLtwist tagged tens of thousands of English-language news articles across 13 entity fields, with relation extraction and coreference resolution. Annotation and processing time fell by more than 50%, and the model's precision on intelligence-relevant information improved.
What happens when the labeling tool can't keep up?
At very high annotation density, labeling tools can lag badly enough that in-tool QA stops being practical. MLtwist extracts the labeled video, rebuilds it in your format, and runs QA on playable video outside the tool, so review doesn't stall.
How do we buy?
Directly, through Carahsoft — MLtwist's master government aggregator — or on Google Cloud Marketplace, where the purchase can count toward your Google Cloud commitments. The public sector page has the details.
Bring us your defense & security data
Tell us the data type, volume, and timeline. We'll scope it with our team, yours, or both — and deliver it versioned, in your format.
Also available through Carahsoft and Google Cloud Marketplace.