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Context-Aware Deep Learning based Indian Footpath Damage Segmentation Dataset for Risk Assessment.

Priti Chakurkar1, Deepali Vora2

  • 1Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, 412115, India.

Scientific Data
|December 8, 2025
PubMed
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This study introduces a new dataset for detecting footpath damage, crucial for preventing pedestrian falls in cities. The dataset, featuring detailed annotations, aids in developing automated infrastructure monitoring systems.

Area of Science:

  • Computer Vision
  • Urban Infrastructure Monitoring
  • Civil Engineering

Background:

  • Damaged footpaths present significant pedestrian safety risks in urban areas.
  • Automated monitoring systems are needed for infrastructure maintenance and fall prevention.
  • Existing datasets may lack the detail or diversity required for robust model training.

Purpose of the Study:

  • To introduce the Indian Footpath Damage Segmentation Dataset for research in automated infrastructure monitoring.
  • To provide a high-resolution, manually annotated dataset for footpath damage detection and severity assessment.
  • To establish a benchmark for evaluating deep learning models in this domain.

Main Methods:

  • Collected a high-resolution image dataset of footpath damage in Pune, India, under varied conditions.

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  • Manually annotated images with pixel-level segmentation masks for footpath damage, including severity levels.
  • Trained and evaluated deep learning models, including U-Net with EfficientNet-B3 and ensemble methods, on the dataset.

    • Main Results:

    • The best-performing ensemble model achieved a Dice score of 0.6899 and an IoU of 0.6741.
    • The model demonstrated strong performance with an accuracy of 0.9317 and an F1-score of 0.6899.
    • The dataset facilitates the development of accurate footpath damage segmentation models.

    Conclusions:

    • The Indian Footpath Damage Segmentation Dataset is a valuable resource for advancing research in pedestrian safety and infrastructure monitoring.
    • The developed models show promising results for automated detection and severity assessment of footpath damage.
    • The dataset is publicly available on Zenodo to encourage further research and development.