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Insights from the Road Damage Detection Challenge Series (2018–2024)

Nature Machine Intelligence volume 7, pages 1768–1769 (2025)Cite this article

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The organizers reflect on how a multi-year, multi-country benchmark aligned AI research in road damage detection with practical and regional constraints, steering it towards deployment relevance.

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Fig. 1: Timeline of the Road Damage Detection Challenges (2018–2024).

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Acknowledgements

We thank the participants and our co-organizers of the RDDC data cups for their contributions. D.A. is supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI grant number 24K17366. We gratefully acknowledge all sources of support.

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Authors and Affiliations

  1. Center for Spatial Information Science, University of Tokyo, Komaba, Tokyo, Japan

    Deeksha Arya & Yoshihide Sekimoto

  2. UrbanX Technologies, Inc., Tokyo, Japan

    Hiroya Maeda

Authors
  1. Deeksha Arya
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  2. Hiroya Maeda
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  3. Yoshihide Sekimoto
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Correspondence to Deeksha Arya.

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Arya, D., Maeda, H. & Sekimoto, Y. Insights from the Road Damage Detection Challenge Series (2018–2024). Nat Mach Intell 7, 1768–1769 (2025). https://doi.org/10.1038/s42256-025-01132-5

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