A model‐based approach for reconstructing a terrain surface from airborne LIDAR data
Bibliographic record
Abstract
Abstract A lidar filtering technique is used to differentiate on‐terrain points and off‐terrain points from a cloud of 3D point data collected by a lidar system. A major issue of concern in this low‐level filter is to design a methodology to ensure a continual adaptation to variations of terrain slopes and object scales. In this paper, a new lidar filtering technique which hierarchically fragments lidar data into piecewise planar terrain models is introduced. Once a number of hypothetical planar terrain models are generated to fit the terrain surface of the underlying area, the optimal terrain model to produce the minimum labelling errors is determined based on minimum description length (MDL) principles. This hypothesis‐verification optimisation is achieved in a coarse‐to‐fine strategy by which the entire terrain surface is incrementally reconstructed by increasing the number of planar terrain models fitted. The proposed technique was successfully applied to a digital surface model provided within an OEEPE lidar trial, showing 0·94% of Type I errors and 6·75% of Type II errors compared to manually classified reference data.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".