Real-world road damage dataset with potholes, cracks, and maintenance holes.

Road surface deterioration poses significant challenges to transportation safety, infrastructure longevity, and timely maintenance planning. Existing street-view datasets are often limited by wide-angle distortions that reduce geometric fidelity and hinder reliable damage analysis. This paper introduces the Road Damage Dataset: Potholes, Cracks, and Manholes, a novel dataset designed for robust detection of road-surface damage in urban and rural settings. The dataset was captured using two consumer-grade devices, acquiring diverse views that mimic real-world deployment situations. It contains high-resolution images with three major and often co-occurring road-damage classes: potholes, cracks, and maintenance holes. It includes 2009 hand-labeled images containing 1261 potholes, 2519 cracks, and 957 maintenance holes with verified bounding boxes. All images were post-processed to improve visual quality and remove sensitive information. The dataset includes several districts in Rome (Italy) and nearby semi-urban and rural towns such as Sacrofano, offering more environmental heterogeneity than many existing datasets. Thanks to its varied capture circumstances, viewing angles, and scene contexts, this dataset supports the development of generalizable models for real-world road-damage detection.