RICO-3D: A Benchmark and Baseline Method for Semantic Segmentation of Urban Roadways

A Canadian Benchmark LiDAR Dataset for Urban Infrastructure and 3D Scene Understanding.

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.

A Road Segmentation Method Based on Dynamic Attention and Cross-Scale Semantic Fusion

To address the poor adaptability to complex scenes, blurred boundary details, and the difficult trade-off between accuracy and computational cost in road segmentation from unmanned aerial vehicle (UAV) imagery, this paper proposes DACS-Net, a lightweight road segmentation model based on U-Net and enhanced by dynamic attention and cross-scale semantic information. The model adopts EfficientNetV2-S as a lightweight and efficient encoder, using compound scaling and MBConv bottleneck structures to capture multi-scale features accurately. A dynamic global-local attention module (GLA) is designed to jointly model global context and local neighborhood details through a dynamic global attention branch and an adaptive local branch. Furthermore, a cross-scale semantic feature fusion module (CSF) is constructed to perform intelligent selection and efficient fusion of multi-stage encoder features through semantic-guided weight allocation, edge-enhanced feature alignment, and lightweight channel compression. Experiments on the public AeroScapes dataset show that the proposed method outperforms mainstream models in Dice, Kappa, and other metrics while maintaining relatively low computational complexity, demonstrating its effectiveness and practical value.

Text-Guided Semantic Segmentation Method for Indoor 3D Point Clouds

Abstract. Point cloud semantic segmentation of indoor environments is a fundamental task in 3D scene understanding. However, existing methods mainly rely on geometric structures and color information, which are prone to error results in scenarios involving occlusion, sparse sampling, and geometrically similar structures. To address this issue, this paper proposes a text-knowledge-guided method for the point cloud semantic segmentation of indoor 3D scene. Built upon RandLA-Net as the baseline, the proposed method first constructs the textual semantic prototypes using multi-template prompts, and further enhances the stability of semantic anchors through periodic prototype refreshing. Then, a cross-modal semantic feature alignment mechanism is introduced at both the shallow and the high-level feature stages. Through feature alignment, bidirectional semantic interaction, and gated fusion, textual priors are progressively injected into the point cloud feature learning process. Finally, the model is jointly trained with a point-wise classification loss, a text-prototype alignment constraint, and a boundary optimization constraint to improve the semantic feature discrimination and segmentation boundary quality. Experimental results on the S3DIS dataset demonstrate that the proposed method achieves 86.8% OA, 81.6% mAcc, and 67.2% mIoU, exhibiting more stable segmentation performance in complex indoor scenes. As a consequence, these results indicate that incorporating textual semantic priors can effectively enhance high-level semantic representations of point clouds, providing a feasible solution for indoor 3D scene understand.

Applications of Street View Images based on Artificial Intelligence: A Comprehensive Survey

Street View Images (SVI) are high-resolution, geo-referenced panoramas that capture real-world environments. Integration of Artificial Intelligence (AI) with SVI enables automated analysis for a range of urban applications including object detection, semantic segmentation, text recognition, scene understanding, and socioeconomic prediction. More than 25 recent AI based SVI studies covering applications in crime prediction, building attribute classification, sidewalk inventory, land price estimation, and environmental monitoring were screened for the survey. Across domains, deep learning architectures such as ResNet, ConvNeXt, and Vision Transformers consistently outperformed traditional machine learning models, with reported accuracies up to 94% for classification and R2 values between 0.62–0.83 for prediction tasks. SVI augmented with other data sources like satellite imagery and Global Information System (GIS), enhanced the model performance and contextual understanding. The Findings highlight the dominance of convolutional and transformer-based networks, emerging interest in graph neural networks, and the need for generalized models and diverse datasets to advance SVI research.

LiDAR Point Cloud Classification by 3D Sparse CNN for large-scale Mobile Laser Scanning

Abstract. Semantic classification is a fundamental step in Mobile Laser Scanning (MLS) point clouds processing, and remains a non-trivial task. In this work, we propose a classification framework based on a 3D Sparse Convolutional Neural Network (SparseCNN) for efficient processing of large-scale MLS data. A coarse-to-fine two-stage pipeline is introduced, where an essential model performs a classification for the entire scene, followed by a refinement stage for detailed ground-surface classes. To enhance robustness under diverse acquisition conditions, both point-wise and scene-wise data augmentation strategies are employed during the training, including rotation, jittering, density perturbation, noise injection, and patch swapping. To account for environmental and sensor variations, wavelength-specific models are trained for both urban and highway scenes. Experimental results on urban and highway datasets demonstrate strong performance, achieving over 90% accuracy for major classes, while ablation studies show that radiometric features are critical for distinguishing material dependent classes, such as traffic signs, and that the proposed augmentation strategies improve performance for challenging object categories, such as pedestrian, which is dynamic and structurally ambiguous.

CTM-Net: 3D object detection from LiDAR point clouds for autonomous driving