Rapid Urban Roadside Tree Inventory Using a Mobile Laser Scanning System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a feasible workflow for use of three-dimensional point clouds acquired by a vehicle-borne mobile laser scanning (MLS) system for urban tree inventory. Extracting geometrical information, such as crown diameter, diameter at breast height (DBH), and tree height, from the MLS point clouds is a challenging task due to huge data volume, occlusions, mixed density, and irregular distribution of points in complex urban environments. The proposed workflow consists of three parts: individual tree cluster extraction, geometric parameter estimation, and tree species classification. The results show that over 93% of the roadside trees were correctly detected with an average error of about 5% in the DBH estimation when compared to field surveys and 78% of the overall accuracy was achieved for the classification of tree species.
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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 | 0.001 |
| 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) | 0.000 | 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 it