A COMPARISON OF TREE SEGMENTATION METHODS USING VERY HIGHDENSITY AIRBORNE LASER SCANNER DATA
Bibliographic record
Abstract
Abstract. Developments of LiDAR technology are decreasing the unit cost per single point (e.g. single-photo counting). This brings to the possibility of future LiDAR datasets having very dense point clouds. In this work, we process a very dense point cloud (~200 points per square meter), using three different methods for segmenting single trees and extracting tree positions and other metrics of interest in forestry, such as tree height distribution and canopy area distribution. The three algorithms are tested at decreasing densities, up to a lowest density of ~5 point per square meter. Accuracy assessment is done using Kappa, recall, precision and F-Score metrics comparing results with tree positions from groundtruth measurements in six ground plots where tree positions and heights were surveyed manually. Results show that one method provides better Kappa and recall accuracy results for all cases, and that different point densities, in the range used in this study, do not affect accuracy significantly. Processing time is also considered; the method with better accuracy is several times slower than the other two methods and increases exponentially with point density. Best performer gave Kappa = 0.7. The implications of metrics for determining the accuracy of results of point positions’ detection is reported. Motives for the different performances of the three methods is discussed and further research direction is proposed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; 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".