# The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale

arXiv: https://arxiv.org/abs/1909.04422 · HTML: https://mlsanity.com/paper/1909.04422/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 30 of 50)

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- [TSCLIP: Robust CLIP Fine-Tuning for Worldwide Cross-Regional Traffic Sign Recognition](https://mlsanity.com/paper/2409.15077/) (2024)
- [Towards Real-time Traffic Sign and Traffic Light Detection on Embedded Systems](https://mlsanity.com/paper/2205.02421/) (2022)
- [Scale generalisation properties of extended scale-covariant and scale-invariant Gaussian derivative networks on image datasets with spatial scaling variations](https://mlsanity.com/paper/2409.11140/) (2024)
- [Mapillary Vistas Validation for Fine-Grained Traffic Signs: A Benchmark Revealing Vision-Language Model Limitations](https://mlsanity.com/paper/2508.02047/) (2025)
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- [Improving traffic sign recognition by active search](https://mlsanity.com/paper/2111.14426/) (2021)
- [CueCAn: Cue Driven Contextual Attention For Identifying Missing Traffic Signs on Unconstrained Roads](https://mlsanity.com/paper/2303.02641/) (2023)
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## Abstract (arXiv)

Traffic signs are essential map features globally in the era of autonomous driving and smart cities. To develop accurate and robust algorithms for traffic sign detection and classification, a large-scale and diverse benchmark dataset is required. In this paper, we introduce a traffic sign benchmark dataset of 100K street-level images around the world that encapsulates diverse scenes, wide coverage of geographical locations, and varying weather and lighting conditions and covers more than 300 manually annotated traffic sign classes. The dataset includes 52K images that are fully annotated and 48K images that are partially annotated. This is the largest and the most diverse traffic sign dataset consisting of images from all over world with fine-grained annotations of traffic sign classes. We have run extensive experiments to establish strong baselines for both the detection and the classification tasks. In addition, we have verified that the diversity of this dataset enables effective transfer learning for existing large-scale benchmark datasets on traffic sign detection and classification. The dataset is freely available for academic research: https://www.mapillary.com/dataset/trafficsign.