| description abstract | Unmanned aerial vehicles (UAVs) have recently become popular in building façade inspections to maintain a safe and well-performed built environment. A camera-equipped UAV system can capture numerous high-resolution façade images for close-up visual inspections. However, in several cases, the multispectrum and spatiotemporal data collected by UAVs are not systematically documented and utilized, which obstructs the automation in the identification, localization, assessment, and tracking of façade anomalies. This paper develops an integrated, computational GIS-based information system to provide automated storage, retrieval, detection, assessment, and documentation of façade anomalies based on UAV-captured data. The developed system creates user-friendly access to diverse professional imagery analysis tools from external artificial intelligence (AI) algorithms. A real-world case was studied to present the procedure and advances in the management and analysis of multisourced inspection data to automate UAV-based façade diagnosis. As a result, the proposed method facilitates the seamless fusion, processing, visualization, and documentation of multimodal inspection data, resulting in convenient analysis with discrepancies measured in decimeters for length, millimeters for width, and centimeters for geoposition. This contributes to the understanding of façade conditions and decision-making of timely maintenance throughout a building’s service lifecycle. | |
