Occlusion-based Methodology for the Classification of Lidar Data
Bibliographic record
Abstract
Lidar systems have been widely adopted for the acquisition of dense and accurate topographic data over extended areas. The level of detail and the quality of the collected point cloud motivated the research community to investigate the possibility of automatic object extraction from such data. Prior knowledge of the terrain surface will improve the performance of object detection and extraction procedures. In this paper, a new strategy for automatic terrain extraction from lidar data is presented. The proposed strategy is based on the fact that sudden elevation changes, which usually correspond to non-ground objects, will cause relief displacements in perspective views. The introduced relief displacements will occlude neighboring ground points. To start the process, we generate a digital surface model (DSM) from the irregular lidar points using an interpolation procedure. The presence of sudden-elevation changes and the resulting occlusions can be discerned by sequentially checking the off-nadir angles to the lines of sight connecting the DSM cells and a pre-defined set of synthesized projection centers. Detected occlusions are then used to identify the occluding points, which are hypothesized to be non-ground points. Surface roughness and discontinuities together with inherent noise in the point cloud will lead to some false hypotheses. Therefore, we use a statistical filter to remove these false hypotheses. The performance of the algorithm has been evaluated and verified using both simulated and real lidar datasets with varying levels of complexity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".