Evaluating Semantic Segmentation–Based Scene Descriptions for Multilane Rural Highway Point Clouds against Ground Truth

This study investigates the use of two transformer-based models for generating semantic scene descriptions from semantically labeled point clouds of multilane rural highway segments, focusing on automating the extraction of detailed infrastructure elements. Two models were employed: Point Transformer v2, and a transformer-based point classification model with self-attention and cross-attention mechanisms, classifying 11 infrastructure classes along three rural highways in Alberta, Canada, capturing detailed point clouds under clear weather conditions. Each model incorporated specialized loss functions to address class imbalances and improve segmentation accuracy through a series of advanced neural network architectures, including self-attention and cross-attention layers to enhance inter-point relationships and model generalization. Two large language models (LLMs), Google Gemini and GPT-4o, were used to generate descriptive text based on the segmented point clouds. The LLMs provided detailed, structured narratives capturing visible highway features. The transformer-based point classification model outperformed Point Transformer v2, achieving an average F1 score of 71%, mean Intersection over Union (mIoU) of 61%, and precision exceeding 90% for critical infrastructure classes such as traffic signs, concrete barriers, light poles, and vegetation. Despite semantic segmentation accuracy differences, both models generated natural language scene descriptions that aligned closely with ground truth, with semantic similarity scores surpassing 80%. This consistency in descriptive accuracy underscores the robustness of both models in producing reliable, contextually relevant text. These results highlight the potential of integrating transformer-based models and LLMs for effective semantic segmentation and text description in rural highway infrastructure.