Real-Time Safe Landing Zone Identification Based on Airborne LiDAR
Bibliographic record
Abstract
Over the past two decades, there has been a growing demand for generating digital surface models (DSMs) in real-time, particularly for aircraft landing in degraded visual environments. Challenging landing environments can hinder a pilot's ability to accurately navigate, see the ground, and avoid obstacles that may lead to equipment damage or loss of life. While many accurate and robust filtering algorithms for airborne laser scanning (ALS) data have been developed, they are typically computationally expensive. Moreover, these filtering algorithms require high execution times, making them unsuitable for real-time applications. This research aims to design and implement an efficient algorithm that can be used in real-time on limited-resource embedded processors without the need for a supercomputer. The proposed algorithm effectively identifies the best safe landing zone (SLZ) for an aircraft/helicopter based on processing 3D LiDAR point cloud data collected from a LiDAR mounted on the aircraft/helicopter. The algorithm was successfully implemented in C++ in real-time and validated using professional software for flight simulation. By comparing the results with maps, this research demonstrates the ability of the developed method to assist pilots in identifying the safest landing zone for helicopters.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".