RoadAb-India 2026: A Multi-Layer Road Abnormality Dataset with Object Annotations, Severity Labels, and Geospatial Context

RoadAb-India 2026 is a multi-layer road abnormality image dataset developed to support computer vision and metadata-aware research on road-condition assessment. The dataset contains 2,074 road-scene images collected from multiple geographic regions in India, covering Tamil Nadu, Delhi, and Maharashtra. The images represent urban, residential, rural, and highway road environments under varied lighting, weather, traffic, pavement, and seasonal conditions. The dataset includes 3,391 object-level annotations distributed across 1,984 images containing visible road abnormalities. An additional 90 images contain no visible road abnormalities and are intentionally retained as negative road-scene samples. These negative samples can support false-positive analysis, background modelling, robustness assessment, and realistic evaluation of road-abnormality detection systems. Visible road abnormalities are represented using normalized object-detection bounding boxes. The dataset covers six abnormality classes: Surface depression Pothole Manhole Road patch failure Crack Miscellaneous abnormality The dataset is organized into four complementary metadata layers: Layer 0 — Raw Image Metadata Contains one record for each of the 2,074 images, including the image identifier, file format, file size, image resolution, collection date, and collection state. Layer 1 — Object Annotation Metadata Contains 3,391 object-level annotation records. Each record identifies an annotated road abnormality and provides its image identifier, abnormality identifier, abnormality class, normalized bounding-box centre coordinates, normalized width, and normalized height. Layer 2 — Semantic Metadata Contains one record for each image and provides semantic and environmental attributes, including abnormality type, severity, road type, weather condition, lighting condition, occlusion level, and abnormality shape. Severity is categorized as minor, moderate, or severe. Abnormality-specific fields are intentionally empty for the 90 negative road-scene images. Layer 3 — Geospatial and Road-Context Metadata Contains one record for each image and provides collection state, district, latitude and longitude grid values, pavement type, traffic density, collection season, nearby road features, slope type, road-width category, and proximity to an intersection. The dataset supports research tasks including: Road abnormality object detection Road abnormality classification Multi-label road-scene classification Abnormality severity prediction Metadata-aware computer vision Context-aware road-condition assessment Geographic and environmental robustness analysis Dataset bias and domain-shift analysis Negative-sample and false-positive evaluation Multi-task and multimodal machine learning The images were manually reviewed and annotated using a consistent road-abnormality labelling protocol. Bounding boxes were designed to cover the visible extent of each abnormality. A quality-control review was conducted on a randomly selected subset of 207 images. Inter-annotator evaluation on the reviewed subset produced a mean bounding-box Intersection over Union of 0.91, class agreement of 96.80%, and Cohen’s kappa of 0.94. All identified annotation corrections were incorporated before the preparation of this release. Duplicate and near-duplicate captures were reviewed and retained only where they represented naturally occurring repeated observations or different viewpoints. Related images were assigned to the same dataset partition during evaluation to avoid train–validation–test leakage. RoadAb-India 2026 is intended to complement larger road-damage datasets by emphasizing annotation richness, severity information, environmental context, and fine-grained geospatial metadata rather than dataset scale alone. The dataset is initially distributed under restricted access while the associated data descriptor manuscript is under preparation and peer review. Access conditions may be updated after publication of the associated article.