CROWD-SOURCED SURVEYING FOR BUILDING ARCHAEOLOGY: THE POTENTIAL OF STRUCTURE FROM MOTION (SFM) AND NEURAL RADIANCE FIELDS (NERF)

Abstract. This contribution presents a simple workflow for surveying historical buildings and sites using crowd-sourced images. The proposed approach involves collecting large datasets of images from the internet using free plugins, followed by automatic image analysis and filtering using AI-based tools. 3D reconstructions are then created with Structure from Motion (SfM) and neural radiance fields (NeRF). To assess the reliability of crowd-sourced surveys, the 3D reconstructions are compared to high-precision laser scans of large medieval churches. In addition, the paper demonstrates the potential of this workflow in the field of building archaeology through detailed geometrical analyses of several iconic domes such as Hagia Sofia. By enabling remote and 4D surveys, crowd-sourced reconstruction methods open up novel opportunities for rapid, affordable and borderless research on cultural heritage.