A multiclassifier convolutional neural network to identify defect type and severity in roofing elements

Purpose Roofing is highly susceptible to environmental damage from elements like wind, snow and rain. Regular inspection and maintenance are essential to extend a roof’s lifespan. This study aims to develop an automated system that detects and classifies roofing damage types and their severity using image-based analysis, helping asset managers prioritize repairs and allocate maintenance resources more effectively. Design/methodology/approach This study uses Convolutional Neural Networks (CNNs) for image-based damage detection and classification. Over 3,000 images of roofing segments (1.5 × 1.12 m) from institutional buildings were used for training and testing. The model first identifies damage type – no damage, vegetation or ponding – then classifies vegetation damage severity into low, moderate or severe. Findings The developed CNN model achieved over 94% accuracy in both damage type and severity classification. The results demonstrate the model’s effectiveness in analyzing roofing defects. Research limitations/implications Future enhancements include expanding the system to detect additional defect types like cracks and flashing defects, offering a scalable solution for systematic roof condition assessment and maintenance planning. Originality/value Unlike traditional manual inspections, this approach uses computer vision techniques to offer a scalable, data-driven framework that identifies damage types and quantifies severity levels. This makes roofing inspections more efficient, consistent and safer.