Visual Safety Mapping for UAV Landings Using Ordinal Regression Networks
Bibliographic record
Abstract
As Unmanned Aerial Vehicles (UAVs) see growing use in civilian applications, reliably identifying Safe Landing Zones (SLZs) in varied environments is essential for autonomous navigation and emergency response. Passive vision sensors offer a low-cost, lightweight solution for real-time terrain analysis and 3D scene reconstruction, making them ideal for onboard systems. We introduce OR-SLZNet, an original deep learning model based on ordinal regression to predict SLZs from UAV imagery. Unlike prior approaches, OR-SLZNet produces dense, multi-level safety maps by jointly leveraging photometric (e.g., color and texture) and geometric cues (e.g., flatness, slope, and depth), assigning each pixel an ordinal safety score that reflects landing suitability. With real-time inference (~0.02s/frame), the model supports onboard deployment and rapid decision-making in time-critical situations. Extensive experiments on five diverse datasets demonstrate OR-SLZNet effectiveness and strong generalization across a wide range of structural complexities. Additional materials and code are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/saba129/OR-SLZNet</uri>.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".