Transfer Learning Between MLS Point Clouds for Vegetation Segmentation
Bibliographic record
Abstract
Vegetation segmentation stands as a critical task in environmental monitoring and management. Mobile laser scanning (MLS) datasets combined with deep learning (DL) techniques have achieved remarkable progress toward precise vegetation segmentation. However, most studies have confined their evaluations to individual MLS datasets, limiting the understanding of model adaptability and generalization across diverse geographical regions. Moreover, achieving generalization across MLS datasets remains challenging due to the inherent differences in vegetation distribution and diversity, as well as scene complexity. Limited availability of labeled point clouds and constraints in computational resources further highlight the importance of research employing multiple MLS datasets. In this study, we investigate fine-tune-based transfer learning (FTL) as an approach to enhance the generalization capability of vegetation segmentation across diverse MLS datasets. With GreenSegNet as the backbone architecture, three MLS datasets, Toronto3D, Chandigarh, and Kerala, have been employed. Direct feature transfer resulted in substandard results, requiring fine-tuning on the target dataset. Notably, fine-tuning gradually improves the performance, even surpassing baselines on simpler datasets. The effectiveness of FTL is strongly influenced by the alignment of feature characteristics between the source and target datasets. Overlapping feature distributions between the source and target datasets demonstrate significant improvements in performance with minimal training data. Overall, the study underscores the potential of FTL as a viable approach for achieving generalization across MLS datasets for vegetation segmentation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".