StreetSurfaceVis: a dataset of crowdsourced street-level imagery annotated by road surface type and quality
Abstract 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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04295-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832594996386070528 |
|---|---|
| author | Alexandra Kapp Edith Hoffmann Esther Weigmann Helena Mihaljević |
| author_facet | Alexandra Kapp Edith Hoffmann Esther Weigmann Helena Mihaljević |
| author_sort | Alexandra Kapp |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-ad3e202874e84342ba98574b2101eb49 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-ad3e202874e84342ba98574b2101eb492025-01-19T12:09:35ZengNature PortfolioScientific Data2052-44632025-01-0112111010.1038/s41597-024-04295-9StreetSurfaceVis: a dataset of crowdsourced street-level imagery annotated by road surface type and qualityAlexandra Kapp0Edith Hoffmann1Esther Weigmann2Helena Mihaljević3Hochschule für Technik und Wirtschaft Berlin (HTW Berlin)Hochschule für Technik und Wirtschaft Berlin (HTW Berlin)Hochschule für Technik und Wirtschaft Berlin (HTW Berlin)Hochschule für Technik und Wirtschaft Berlin (HTW Berlin)Abstract 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.https://doi.org/10.1038/s41597-024-04295-9 |
| spellingShingle | Alexandra Kapp Edith Hoffmann Esther Weigmann Helena Mihaljević StreetSurfaceVis: a dataset of crowdsourced street-level imagery annotated by road surface type and quality Scientific Data |
| title | StreetSurfaceVis: a dataset of crowdsourced street-level imagery annotated by road surface type and quality |
| title_full | StreetSurfaceVis: a dataset of crowdsourced street-level imagery annotated by road surface type and quality |
| title_fullStr | StreetSurfaceVis: a dataset of crowdsourced street-level imagery annotated by road surface type and quality |
| title_full_unstemmed | StreetSurfaceVis: a dataset of crowdsourced street-level imagery annotated by road surface type and quality |
| title_short | StreetSurfaceVis: a dataset of crowdsourced street-level imagery annotated by road surface type and quality |
| title_sort | streetsurfacevis a dataset of crowdsourced street level imagery annotated by road surface type and quality |
| url | https://doi.org/10.1038/s41597-024-04295-9 |
| work_keys_str_mv | AT alexandrakapp streetsurfacevisadatasetofcrowdsourcedstreetlevelimageryannotatedbyroadsurfacetypeandquality AT edithhoffmann streetsurfacevisadatasetofcrowdsourcedstreetlevelimageryannotatedbyroadsurfacetypeandquality AT estherweigmann streetsurfacevisadatasetofcrowdsourcedstreetlevelimageryannotatedbyroadsurfacetypeandquality AT helenamihaljevic streetsurfacevisadatasetofcrowdsourcedstreetlevelimageryannotatedbyroadsurfacetypeandquality |