Monitoring Critical Infrastructure Using 3D LiDAR Point Clouds
Bibliographic record
Abstract
Monitoring critical infrastructure is of great importance for resilience against current and emerging hazards. In Canada, at the national level, critical infrastructure is strategically classified into the following 10 sectors: energy and utilities, finance, food, transportation, government, information and communication technology, health, water, safety, and manufacturing. Many of these critical infrastructures may be effectively monitored using emerging technologies such as light detection and ranging (LiDAR). LiDAR technology enables active remote sensing from airborne and terrestrial systems. Accordingly, LiDAR can be used in a wide range of applications related to the monitoring of critical infrastructure to promote resilience. This survey provides a comprehensive, and structured literature overview of LiDAR technology currently available for commercial and research use, and also provides a detailed review of the relevant applications of LiDAR for critical infrastructure monitoring. Several LiDAR-based applications in areas of critical infrastructure such as terrestrial and air transportation, gas, oil, and water pipelines, energy generation and distribution, and security are discussed. A summary of the LiDAR data sets that are currently available for use is also presented.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".