  In this paper, we present a case study that performs an unmanned aerial
vehicle (UAV) based fine-scale 3D change detection and monitoring of
progressive collapse performance of a building during a demolition event.
Multi-temporal oblique photogrammetry images are collected with 3D point clouds
generated at different stages of the demolition. The geometric accuracy of the
generated point clouds has been evaluated against both airborne and terrestrial
LiDAR point clouds, achieving an average distance of 12 cm and 16 cm for roof
and facade respectively. We propose a hierarchical volumetric change detection
framework that unifies multi-temporal UAV images for pose estimation (free of
ground control points), reconstruction, and a coarse-to-fine 3D density change
analysis. This work has provided a solution capable of addressing change
detection on full 3D time-series datasets where dramatic scene content changes
are presented progressively. Our change detection results on the building
demolition event have been evaluated against the manually marked ground-truth
changes and have achieved an F-1 score varying from 0.78 to 0.92, with
consistently high precision (0.92 - 0.99). Volumetric changes through the
demolition progress are derived from change detection and have shown to
favorably reflect the qualitative and quantitative building demolition
progression.
