GIS-Based Modeling of Multi-Sourced Image Data Collected for Building Facade Inspection

There has been a trend in applying imaging techniques in building facade inspection, such as laser scanning, infrared thermography, high definition camera, etc. Utilizing such techniques creates a large amount of multi-sourced image data for building facade inspections. However, there is a lack of an effective way to manage these massive image data in aspects of spectral, spatial, and temporal data. As GIS is specialized in the storage and analysis of spatial and raster data, it can provide a good working platform for archiving and processing massive image data. This paper developed a framework of a GIS-based data management platform for multi-sourced building facade imagery data including drone-captured high definition images, laser-scanned point clouds, and infrared temperature data, with technical solutions and workflow for image transformation and georegistration. A case study was also presented to demonstrate the organization and processing of the multi-sourced images using the developed platform. As a result of this study, a GIS-based building facade model was created to store multi-sourced image data, which paves the way towards automated detections and assessments of facade anomalies.