AUTOMATED UAV-BASED VIDEO EXPLOITATION FOR MAPPING AND SURVEILLANCE
Bibliographic record
Abstract
Airborne intelligence, surveillance and reconnaissance (ISR) can provide essential information for both military and civilian applications. Such capabilities are critical for situational awareness, mapping and monitoring, and mission planning. UAVs gather large amounts of video data, but it is extremely labour-intensive for operators to analyse the hours and hours of data collected. At MDA, we have developed a suite of video exploitation tools to process UAV video data, including tools for mosaicking, change detection and 3D reconstruction. The main objective of this work is to improve the robustness of these tools, to integrate these tools within a standard GIS framework, to automate the tools, and to present these tools in user-friendly ways to operational users. Our mosaicking tool produces a 2D map from the sequence of video frames contained in a video clip. For scene monitoring applications, our change detection tool identifies differences between two videos taken from two passes of the same terrain at different times. Our 3D reconstruction tools create calibrated geo-referenced photo-realistic 3D models, which are useful for situational awareness. The semi-automated approach allows the user to create surfaces interactively by marking correspondences between frames, while the automated approach generates 3D models automatically. MDA provides UAV services to the Canadian and Australian forces, offering important ISR information to front-line soldiers. Our suite of semi and fully automated video exploitation tools can aid the operators to analyse the huge amount of UAV video data to support battle-space mapping and monitoring applications. 1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".