Scan Line Void Filling of Airborne LiDAR Point Clouds for Hydroflattening DEM
Bibliographic record
Abstract
Generation of LiDAR-derived digital elevation model (DEM), particularly for hydrologic and shore environments, poses a continuous challenge. The presence of laser dropouts found on the water bodies causes data voids/holes in the airborne LiDAR data point clouds. Unnatural huge triangular artifacts may appear in these regions when a DEM is generated, resulting in not only unpleasant visual effect but also inaccurate terrain analyses. The United States Geological Survey has stressed the need of having a hydro-flattened DEM in the LiDAR Base Specification. Different forms of water bodies should be represented by a flat surface. Existing approaches mainly rely on the use of ancillary data or manual intervention during the hydroflattening process. In this study, an automatic data processing workflow is proposed to: 1) classify land and water data points collected by a topographic airborne LiDAR system based on the scan line intensity-elevation ratio; 2) perform scan line void filling of data points in close-to-nadir region and at both swath edges; 3) generate a virtual water surface based on the classified water data points; 4) perform hydroflattening on the DEM. The proposed workflow was examined using five datasets collected by topographic airborne LiDAR on the inland ponds and lakes, inland rivers, nontidal boundary water bodies, tidal water bodies, and islands, as addressed in the LiDAR Base Specification. The results showed that the proposed workflow can successfully generate hydroflattened DEMs and overcome the drawback of existing approaches.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".