L52194 Detection of Third Party Encroachment Using Satellite Based Remote Sensing Technologies
Bibliographic record
Abstract
Building on past experience, it was the objective of this investigation to automate the satellite-based detection of encroachment events, to improve target detection and reduce false alarms using radar and optical imagery and to investigate the integration of one-call services into the process flow. Algorithm development for target detection using optical imagery was carried out with the intention to facilitate the future integration of unmanned airborne vehicle (UAV) technology into the process. The capacity of the multitemporal algorithm was extended to enable the detection of area changes in addition to vehicle targets. The integration of existing notification services in the satellite-based approach was examined. A satellite-based encroachment monitoring system is now in place to undertake large-scale field demonstrations over 100 to 200 miles of right-of-way for a period of several months, preceded by a pre-service calibration phase of several weeks to adjust the procedures to local conditions. A constant false alarm rate between 5 and 10% can be achieved after a service period of 8 to 10 months.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".