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

TL;DR

This paper introduces a UAV-based 3D change detection framework for monitoring progressive building collapse, achieving high accuracy and F-1 scores, enabling detailed analysis of demolition progress using multi-temporal oblique photogrammetry.

Contribution

It presents a hierarchical volumetric change detection method that operates on full 3D datasets without ground control points, specifically tailored for progressive scene changes like building demolition.

Findings

Achieved an average geometric accuracy of 12-16 cm in point clouds.

F-1 scores ranged from 0.78 to 0.92 in change detection.

High precision (0.92-0.99) in identifying true changes.

Abstract

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.