Deforestation: Extracting 3D Bare-Earth Surface from Airborne LiDAR Data
Bibliographic record
Abstract
Bare-earth identification selects points from a LiDAR point cloud so that they can be interpolated to form a representation of the ground surface from which structures, vegetation, and other cover have been removed. We triangulate the point cloud and segment the triangles into flat and steep triangles using a Discriminative Random Field (DRF) that uses a data-dependent label smoothness term.Regions are classified into ground and non-ground based on steepness in the regions and ground points are selected as points on ground triangles. Various post-processing steps are used to further identify flat regions as rooftops and treetops, and eliminate isolated features that affect the surface interpolation.The performance of our algorithm is evaluated in its effectiveness at labeling ground points and, more importantly, at determining the extracted bare-earth surface. Extensive comparison shows the effectiveness of the strategy at selecting ground points leading to good fit in the triangulated mesh derived from the ground points.
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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) | 0.003 | 0.003 |
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; both teacher heads agree on what is shown here.
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".