Modelling of Terrain Surfaces Using Aerial Radar Mapping
Bibliographic record
Abstract
Aerial scene mapping is often done via visual methods, where many 2D images are combined together to create 3D maps. There are several disadvantages to this approach, however, including weather interference, inconsistent or nonexistent lighting, or fast-moving objects, which often appear as either noise or a blur on the created map. Radar technology is mostly used in automotive applications, like lane keeping and adaptive cruise control. It can also be used, however, for aerial mapping of scenes, by mounting the radar to a drone. The radar can then be used to generate a point cloud, that can either replace or complement point clouds and maps from other sensors. We present an approach for generating a point cloud map using aerial radar. We then present an algorithm for aggregation of points based on pose, and a method to create accurate meshes over mapped terrain, to facilitate path planning by ground-based robots.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".