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

arXiv: https://arxiv.org/abs/2108.02800 · HTML: https://mlsanity.com/paper/2108.02800/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 32 references that are in the map.
- First posted on arXiv 5 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [TEASER: Fast and Certifiable Point Cloud Registration](https://mlsanity.com/paper/2001.07715/) (2020)
- [Fast and Robust Iterative Closest Point](https://mlsanity.com/paper/2007.07627/) (2020)

## Built on by (most cited first, 7 of 7)

- [Inferring 3D change detection from bitemporal optical images](https://mlsanity.com/paper/2205.15903/) (2022)
- [Multi-tiling Neural Radiance Field (NeRF) -- Geometric Assessment on Large-scale Aerial Datasets](https://mlsanity.com/paper/2310.00530/) (2023)
- [Classification of structural building damage grades from multi-temporal photogrammetric point clouds using a machine learning model trained on virtual laser scanning data](https://mlsanity.com/paper/2302.12591/) (2023)
- [Engineering deep learning methods on automatic detection of damage in infrastructure due to extreme events](https://mlsanity.com/paper/2205.02125/) (2022)
- [Large-scale DSM registration via motion averaging](https://mlsanity.com/paper/2405.19442/) (2024)
- [3dSAGER: Geospatial Entity Resolution over 3D Objects (Technical Report)](https://mlsanity.com/paper/2511.06300/) (2025)
- [Uncertainty Quantification Framework for Aerial and UAV Photogrammetry through Error Propagation](https://mlsanity.com/paper/2507.13486/) (2025)

## Abstract (arXiv)

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.