Industries / Defense & Security
Defense & SecurityDefense and security AI data, checked where it matters.
Detection, segmentation and tracking data from EO/IR, full-motion video and multi-sensor collections. Models take the repetitive passes; trained people resolve the small, distant, partly hidden and ambiguous objects that decide whether a system can be trusted.
The data problem.
Defense imagery breaks the usual assumptions: clean views, large objects, stable classes.
Objects a few pixels wide
Vehicles, vessels and people at long range or from altitude, where a box two pixels off changes what the model learns.
One scene, many sensors
Day and thermal channels, haze, glare, motion blur and link compression, each changing how a class looks.
Fine-grained, changing classes
Look-alikes, partial views and programme-specific definitions that need written rules, not guesses.
What we label.
- EO and IR stills from airborne, ground or maritime platforms
- Full-motion video (FMV), including long sequences
- Oblique and overhead aerial imagery
- Multi-sensor captures of one scene, with metadata
- Detection with boxes and rotated boxes
- Instance and semantic segmentation
- Multi-object tracking with persistent IDs through occlusion
- Attributes: state, orientation, partial visibility
- Validation of existing model outputs against acceptance criteria
How it is checked.
Approaches we use or evaluate: tiled detection for small objects, segmentation proposals, keyframe tracking and IR contrast processing. People review where evidence is weakest:
- Low-confidence detections and objects near the sensor’s size limit
- Disagreement between models, or with the reference set
- Track breaks, ID swaps and re-entries in long video
- Look-alike and partly visible objects, against the written definitions
- A sample of auto-accepted items, so acceptance stays honest
One oblique frame, structured.
An aerial photo of a causeway pier: causeway segmented, crane and container groups boxed, a staging area outlined, one uncertain object routed to a person.
Point AI’s role.
The people who see your data are Point AI employees, trained in-house; no clearances or certifications are claimed here. How we handle data
- Calibration on representative samples to fix classes, edge cases and acceptance criteria
- Managed operations by one trained team that stays on the programme
- Delivery in Point AI Studio: review in the browser, export or API
Have a challenging data problem?
Tell us what you’re building and what data you have. We’ll suggest a practical way to process, check and deliver it.