StreetSurfaceVis: a dataset of crowdsourced street-level imagery annotated by road surface type and quality
This addresses road surface assessment for vulnerable traffic participants like cyclists and wheelchair users, but is incremental as it focuses on dataset creation and annotation efficiency.
The authors tackled the problem of road unevenness affecting traffic safety by introducing StreetSurfaceVis, a dataset of 9,122 street-level images annotated for road surface type and quality, and proposed a sampling strategy that reduced manual annotation workload while ensuring sufficient class representation.
Road unevenness significantly impacts the safety and comfort of traffic participants, especially vulnerable groups such as cyclists and wheelchair users. To train models for comprehensive road surface assessments, we introduce StreetSurfaceVis, a novel dataset comprising 9,122 street-level images mostly from Germany collected from a crowdsourcing platform and manually annotated by road surface type and quality. By crafting a heterogeneous dataset, we aim to enable robust models that maintain high accuracy across diverse image sources. As the frequency distribution of road surface types and qualities is highly imbalanced, we propose a sampling strategy incorporating various external label prediction resources to ensure sufficient images per class while reducing manual annotation. More precisely, we estimate the impact of (1) enriching the image data with OpenStreetMap tags, (2) iterative training and application of a custom surface type classification model, (3) amplifying underrepresented classes through prompt-based classification with GPT-4o and (4) similarity search using image embeddings. Combining these strategies effectively reduces manual annotation workload while ensuring sufficient class representation.