GreenSegNet: A Novel Deep Learning Architecture for Urban Vegetation Segmentation From MLS Data
Bibliographic record
Abstract
Deep learning (DL) models combined with mobile laser scanning (MLS) datasets have demonstrated immense potential for vegetation segmentation. However, restricted performance and inconsistent behavior across datasets by generic DL models offer notable concerns. Furthermore, to capture the characteristic distribution of vegetation points toward effective segregation, a dedicated model for vegetation segmentation is essential. In addition, with curated class-specific DL models being conceptualized, the same is indispensable for vegetation. To address this problem, we propose a novel DL architecture, green segmentation network (GreenSegNet), tailored for vegetation segmentation from MLS point cloud data. Toward a comprehensive assessment, GreenSegNet has been investigated on MLS datasets from three study sites, Chandigarh, Toronto3D, and Kerala. GreenSegNet has illustrated state of the art (SOTA) as well as consistent segmentation performance across all the datasets. GreenSegNet has achieved mean intersection over union (mIoU) as follows: Chandigarh 96.43%, Toronto3D 92.70%, and Kerala 90.16%. In addition, with less than one million parameters, the architecture is the most efficient with respect to the number of parameters among the representative DL models. The associated ablation studies conform to the effectiveness of GreenSegNet. Unlike other SOTA models, GreenSegNet is found robust across different datasets and terrains.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".