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

A volumetric change detection framework using UAV oblique photogrammetry  - A case study of ultra-high-resolution monitoring of progressive building  collapse