Intelligent Recognition of Building Facade Defects: A Multilevel Review from Visual Perception to Engineering...

Building facades are continually exposed to weathering, material aging, hygrothermal cycling, and service-related disturbances. Cracks, delamination, spalling, and seepage can compromise durability and safety, while associated thermal anomalies provide indirect evidence of deterioration. Deep learning, UAV inspection, infrared thermography, LiDAR, building information modeling (BIM), and digital twins have shifted facade inspection toward automated sensing and spatially referenced assessment. Yet most studies remain focused on isolated measures of model accuracy and give limited attention to the full pathway from detection and quantification to component localization, condition rating, and repair. This review organizes the evidence along five dimensions: annotation granularity (A0-A2/A2*), visual task hierarchy (T1-T5), fusion level (F0-F3), spatial mapping level (S0-S3), and engineering maturity (M1-M3). The synthesis indicates that acquisition conditions, material heterogeneity, negative-sample composition, and annotation rules constrain model generalization. Single-modality methods cover classification, detection, segmentation, and partial geometric quantification, but facade-specific external validation remains limited. Multimodal gains depend on registration quality, thermophysical conditions, defect mechanisms, and sensor reliability. Mapping two-dimensional outputs to point clouds, BIM, and digital twins is technically feasible, but error propagation, component matching, and condition-rating protocols remain inconsistent. Interpretability tools and vision foundation models may support verification, annotation, and reporting but cannot replace dedicated systems with validated error bounds. Future work should prioritize cross-dataset benchmarks, facade-specific lightweight models, reproducible multimodal evaluation, standardized 2D-to-3D accuracy protocols, uncertainty-aware manual review, and engineering condition-rating frameworks.