Individual Tree Crown Delineation Using Multispectral LiDAR Data
Bibliographic record
Abstract
In this study, multispectral Light Detection and Ranging (LiDAR) data were utilized to improve delineation of individual tree crowns (ITC) as an important step in individual tree analysis. A framework to integrate spectral and height information for ITC delineation was proposed, and the multi-scale algorithm for treetop detection developed in one of our previous studies was improved. In addition, an advanced region-based segmentation method that used detected treetops as seeds was proposed for segmentation of individual crowns based on their spectral, contextual, and height information. The proposed methods were validated with data acquired using Teledyne Optech's Titan LiDAR sensor. The sensor was operated at three wavelengths (1550 nm, 1064 nm, and 532 nm) within a study area located in the city of Toronto, ON, Canada. The proposed method achieved 80% accuracy, compared with manual delineation of crowns, considering both matched and partially matched crowns, which was 12% higher than that obtained by the earlier marker-controlled watershed (MCW) segmentation technique. Furthermore, the results showed that the integration of spectral and height information improved ITC delineation using either the proposed framework or MCW segmentation, compared with using either spectral or height information individually.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.002 |
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 teacher head, 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".