A Canadian Benchmark LiDAR Dataset for Urban Infrastructure and 3D Scene Understanding
4 affiliations
- 1Graduate Research Assistant, University of Alberta, Edmonton, Alberta Canada
- 2Postdoctoral Fellow, University of Alberta, Edmonton, Alberta Canada
- 3City of Edmonton Research Chair in Urban Traffic Safety, Edmonton, Canada
- 4Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Canada
Abstract
High-quality 3D perception is essential for autonomous vehicles, urban analytics, and the development of intelligent transportation systems. However, existing LiDAR datasets are limited in their representation of fine-grained roadway and pedestrian infrastructure, and geographic diversity, particularly for environments common in North American cities. This paper introduces YEG3D, a large-scale, point-wise annotated mobile laser scanning (MLS) dataset comprising more than 682 million points collected across 14 km of urban roadway in Edmonton, Canada. The dataset includes a fine-grained taxonomy of 18 semantic classes, with an emphasis on detailed pedestrian, cyclist, and roadway infrastructure rarely distinguished in existing benchmarks. We additionally present a comprehensive baseline evaluation using five state-of-the-art semantic segmentation models, including PointNet++, DGCNN, KPConv, KPConvX, and Point Transformer v3. Among the evaluated models, Point Transformer V3 achieves the strongest overall performance, attaining 81.8% overall accuracy, 46.2% mean Intersection over Union (mIoU), and 56.8% mean F1 score, outperforming all other architectures across both global and class-level metrics. Detailed confusion matrix analysis reveals that while large structural classes are segmented reliably, fine-grained elements such as markings, bike lanes, and crosswalks remain challenging due to sparsity, occlusion, and class imbalance. YEG3D provides a new foundation for advancing research in 3D semantic segmentation, urban perception, and infrastructure-aware autonomous systems, and will be expanded in future releases to broaden its geographic and semantic coverage.
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BibTeX
@article{sakr2026canadian,
title = {{A Canadian Benchmark LiDAR Dataset for Urban Infrastructure and 3D Scene Understanding}},
author = {Amr M. Sakr and Fiseha Birhane and Ehab K. Mahmoud and Karim El-Basyouny},
journal = {Scientific Data},
year = {2026},
month = jul,
doi = {10.1038/s41597-026-07830-y},
url = {https://doi.org/10.1038/s41597-026-07830-y},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - A Canadian Benchmark LiDAR Dataset for Urban Infrastructure and 3D Scene Understanding
AU - Amr M. Sakr
AU - Fiseha Birhane
AU - Ehab K. Mahmoud
AU - Karim El-Basyouny
PY - 2026
DA - 2026/07/07
JO - Scientific Data
DO - 10.1038/s41597-026-07830-y
UR - https://doi.org/10.1038/s41597-026-07830-y
AB - High-quality 3D perception is essential for autonomous vehicles, urban analytics, and the development of intelligent transportation systems. However, existing LiDAR datasets are limited in their representation of fine-grained roadway and pedestrian infrastructure, and geographic diversity, particularly for environments common in North American cities. This paper introduces YEG3D, a large-scale, point-wise annotated mobile laser scanning (MLS) dataset comprising more than 682 million points collected across 14 km of urban roadway in Edmonton, Canada. The dataset includes a fine-grained taxonomy of 18 semantic classes, with an emphasis on detailed pedestrian, cyclist, and roadway infrastructure rarely distinguished in existing benchmarks. We additionally present a comprehensive baseline evaluation using five state-of-the-art semantic segmentation models, including PointNet++, DGCNN, KPConv, KPConvX, and Point Transformer v3. Among the evaluated models, Point Transformer V3 achieves the strongest overall performance, attaining 81.8% overall accuracy, 46.2% mean Intersection over Union (mIoU), and 56.8% mean F1 score, outperforming all other architectures across both global and class-level metrics. Detailed confusion matrix analysis reveals that while large structural classes are segmented reliably, fine-grained elements such as markings, bike lanes, and crosswalks remain challenging due to sparsity, occlusion, and class imbalance. YEG3D provides a new foundation for advancing research in 3D semantic segmentation, urban perception, and infrastructure-aware autonomous systems, and will be expanded in future releases to broaden its geographic and semantic coverage.
SN - 2052-4463
ER -