Multi-class Road-Surface Condition Dataset for Automated Pothole and Road-Damage Classification

Published: 12 August 2026| Version 1 | DOI: 10.17632/c43y93xswd.1
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Description

### Dataset Description This dataset is a multi-class road-surface image dataset developed for automated **road damage detection and classification in Bangladesh**. It contains **7,489 RGB crop images** derived from **3,154 original road images**. The source images were collected through two complementary approaches: **Google Earth imagery**, which provided broader coverage of road conditions, and **smartphone camera photographs** captured from real-world roads to represent practical and diverse surface conditions. Each visible road-damage region was annotated using **smart polygon-based instance segmentation**, providing detailed boundaries of individual damage regions. The polygon annotations were used to isolate each damage area from the original image, and the extracted regions were resized to **224 × 224 pixels** and stored as RGB images. This process produced one crop for each annotated damage instance. The dataset consists of **five road-damage categories and one background category**: * **Cracks:** Linear or irregular fractures appearing on the road surface. * **Damaged Asphalt:** Broken, worn, deteriorated, or spalled asphalt pavement. * **Open Manhole:** Exposed or uncovered manholes present on the road surface. * **Potholes:** Depressed or bowl-shaped holes resulting from pavement deterioration. * **Water-Filled Potholes:** Potholes containing standing water, which may partially obscure the damaged region. * **Background:** Clean and defect-free road-surface regions used as a rejection class. The dataset contains **1,689 Cracks, 1,830 Damaged Asphalt, 126 Open Manhole, 1,324 Potholes, 1,272 Water-Filled Potholes, and 1,248 Background samples**, totaling **7,489 images**. The dataset was designed to support **deep-learning-based road damage classification**, including individual CNN/transformer backbones and **multi-backbone ensemble models**. It can also be used for research on road-condition assessment, automated infrastructure monitoring, road damage severity analysis, and intelligent route recommendation systems.

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Computer Vision, Image Segmentation, Transportation Engineering, Multi-Classifiers, Road Safety Standard, Image Classification, Surface Damage, Deep Learning, Damage Classification

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