  Road unevenness significantly impacts the safety and comfort of various
traffic participants, especially vulnerable road users such as cyclists and
wheelchair users. This paper introduces StreetSurfaceVis, a novel dataset
comprising 9,122 street-level images collected from a crowdsourcing platform
and manually annotated by road surface type and quality. The dataset is
intended to train models for comprehensive surface assessments of road
networks. Existing open datasets are constrained by limited geospatial coverage
and camera setups, typically excluding cycleways and footways. By crafting a
heterogeneous dataset, we aim to fill this gap and enable robust models that
maintain high accuracy across diverse image sources. However, the frequency
distribution of road surface types and qualities is highly imbalanced. We
address the challenge of ensuring sufficient images per class while reducing
manual annotation by proposing a sampling strategy that incorporates various
external label prediction resources. 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 or
similarity search using image embeddings. We show that utilizing a combination
of these strategies effectively reduces manual annotation workload while
ensuring sufficient class representation.
