Contextual and video data needed for Subjective Cyclist Experience modelling

This dataset contains contextual GIS data needed for the analysis, manual, and LLM-based video data annotations (excluding videos themselves). The dataset includes four main components: Video contextual data – open-source GIS layers with metadata that define video context. Manually-labelled video data – human-annotated labels generated through manual annotation, providing curated and expert-informed interpretations of video content. LLM-labeled video data – labels automatically generated using Large Language Models (LLMs), intended for comparison with human annotations and exploration of automated labeling approaches. Video ground truth – finalized reference labels used for evaluation and benchmarking, derived from LLM annotations, manual annotations, and geospatial operations. The dataset is suitable for research in video analysis, machine learning, multimodal learning, and annotation quality comparison. The dataset does not include videos themselves.