YUTO SEMANTIC: A LARGE SCALE AERIAL LIDAR DATASET FOR SEMANTIC SEGMENTATION
Bibliographic record
Abstract
Abstract. Creating virtual duplicates of the real world has garnered significant attention due to its applications in areas such as autonomous driving, urban planning, and urban mapping. One of the critical tasks in the computer vision community is semantic segmentation of outdoor collected point clouds. The development and research of robust semantic segmentation algorithms heavily rely on precise and comprehensive benchmark datasets. In this paper, we present the York University Teledyne Optech 3D Semantic Segmentation Dataset (YUTO Semantic), a multi-mission large-scale aerial LiDAR dataset specifically designed for 3D point cloud semantic segmentation. The dataset comprises approximately 738 million points, covering an area of 9.46 square kilometers, which results in a high point density of 100 points per square meter. Each point in the dataset is annotated with one of nine semantic classes. Additionally, we conducted performance tests of state-of-the-art algorithms to evaluate their effectiveness in semantic segmentation tasks. The YUTO Semantic dataset serves as a valuable resource for advancing research in 3D point cloud semantic segmentation and contributes to the development of more accurate and robust algorithms for real-world applications. The dataset is available at https://github.com/Yacovitch/YUTO_Semantic.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.010 |
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".