Multivariate Gaussian Decomposition for Multispectral Airborne Lidar Data Classification
Bibliographic record
Abstract
Multispectral airborne LiDAR technology has been available since 2014 by the first commercial multispectral airborne LiDAR sensor, Optech Titan. The sensor acquires LiDAR data at three independent wavelengths (1550, 1064 and 532 nm). This allows for the collection of a diversity of spectral information from different land objects. Recent studies have been devoted to use the spectral information of the LiDAR data along with the elevation information for classification purposes. In this paper, we present an automatic classification method for multispectral airborne LiDAR data based on the multivariate Gaussian decomposition (MVGD). A data subset covering an urban area in Oshawa, Ontario, Canada was used to test the method. The proposed method achieved an overall accuracy of 95.6% for classifying the multispectral LiDAR data into four different classes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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 | 0.001 |
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".