AI-Based Quantification of Crack Geometry on Retaining Walls from Mobile Earth-Observation Imagery

Cracks on retaining walls and road surfaces can reveal the early warning signs of geohazards such as landslides or slumps in rural areas. However, even today, many governments still rely on manual visual inspection to identify and evaluate cracks, which is time-consuming, subjective, and highly dependent on individual experience. Artificial intelligence (AI) applied to Earth-observation imagery not only enables the detection of potentially dangerous cracks but also makes it possible to quantify their geometric properties, providing a more objective and quantitative basis for infrastructure monitoring and geohazard risk management.

Nevertheless, several key challenges remain. First, although recent studies have developed many advanced algorithms for crack detection and segmentation, methods for measuring crack width, length ,and area are still insufficient. Second, most existing models are designed for road cracks, while cracks on retaining walls present more complex textures, illumination conditions, and background noise, requiring dedicated model fine-tuning. Third, in regions with dense vegetation, branches, leaves, and shadows often produce false detections, making it difficult for AI models to distinguish real cracks from environmental interference.

In this study, we aim to quantify crack geometry from mobile panoramic Earth-observation imagery and to develop an AI model optimized for cracks on retaining walls in complex environments. A multi-stage approach is used to combine YOLO-based crack detection with 3D geospatial information for estimating the length, width, and area of individual cracks. By focusing on real cracks under vegetation-rich and noisy conditions, this approach advances AI-based quantitative analysis of surface degradation. These crack metrics provide a foundation for future retaining wall stability assessment and risk-informed infrastructure management.