Automated Collection Planning for Civilian and Commercial Satellite Imagery, and Definition and Exploitation of the Collection Asset Specification Data Structure
Bibliographic record
Abstract
There are more than 8000 traditional and small/microsatellites in low Earth orbit (LEO) and many of these are civilian and commercial satellites for remote sensing and space-based intelligence, surveillance and reconnaissance (ISR). Collection planning is the first step in the tasking, collection, processing, exploitation, and dissemination (TCPED) process and is required to choose the collection assets (satellites), instrument modes, and orbital passes that best match the collection task. Collection planning requires understanding and experience with: requirements; satellite and instrument phenomenologies, and capabilities; collection strategies; and data processing and exploitation methodologies. Given this, it is challenging for collection managers to make the best use of available satellites in the time available, and they would benefit from automation in the collection planning processes and systems. This article defines and describes collection planning terminology, notation, and processes. It defines new metrics for assessing the temporal coverage (completeness and density of collection opportunities along the time axis), and it describes six semiautomated tools and their underlying algorithms. These can be used by a collection manager to automate elements of the collection planning process, and they can be used for machine-to-machine communication using web services, thereby decreasing the total time required. This machine-to-machine communication permits the collection planning process to be completed in seconds instead of minutes or hours, time which can be critical for dynamic tasking such as tip and cue or last-minute retasking situations.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".