To extend the StreetSurfaceVis dataset beyond Germany, several strategies can be employed. First, leveraging crowdsourcing platforms similar to Mapillary in other countries can facilitate the collection of street-level imagery. Collaborating with local organizations or universities can help in gathering images from diverse geographic regions, ensuring a heterogeneous dataset that reflects various road conditions and user experiences.
Second, integrating data from OpenStreetMap (OSM) in different countries can provide valuable metadata about road surface types and qualities, which can be used to pre-label images. This approach can be complemented by utilizing machine learning models trained on the existing dataset to classify road surfaces in new regions, thereby reducing the need for extensive manual annotation.
Third, partnerships with local governments or transportation agencies can enhance data collection efforts, as they may have access to existing road condition assessments and imagery. Additionally, expanding the dataset to include various weather conditions and times of day can improve the robustness of models trained on this data, making them more applicable to a wider range of environments.