Liu, Ziwen;
Brigham, Rosie;
Long, Emily Rosemary;
Wilson, Lyn;
Frost, Adam;
Orr, Scott Allan;
Grau-Bové, Josep;
(2022)
Semantic segmentation and photogrammetry of crowdsourced images to monitor historic facades.
Heritage Science
, 10
(1)
, Article 27. 10.1186/s40494-022-00664-y.
Preview |
Text
Liu_et_al-2022-Heritage_Science.pdf - Published Version Download (6MB) | Preview |
Abstract
Crowdsourced images hold information could potentially be used to remotely monitor heritage sites, and reduce human and capital resources devoted to on-site inspections. This article proposes a combination of semantic image segmentation and photogrammetry to monitor changes in built heritage sites. In particular, this article focuses on segmenting potentially damaging plants from the surrounding stone masonry and other image elements. The method compares different backend models and two model architectures: (i) a one-stage model that segments seven classes within the image, and (ii) a two-stage model that uses the results from the first stage to refine a binary segmentation for the plant class. The final selected model can achieve an overall IoU of 66.9% for seven classes (54.6% for one-stage plant, 56.2% for two-stage plant). Further, the segmentation output is combined with photogrammetry to build a 3D segmented model to measure the area of biological growth. Lastly, the main findings from this paper are: (i) With the help of transfer learning and proper choice of model architecture, image segmentation can be easily applied to analyze crowdsourcing data. (ii) Photogrammetry can be combined with image segmentation to alleviate image distortions for monitoring purpose. (iii) Beyond the measurement of plant area, this method has the potential to be easily transferred into other tasks, such as monitoring cracks and erosion, or as a masking tool in the photogrammetry workflow.
| Type: | Article |
|---|---|
| Title: | Semantic segmentation and photogrammetry of crowdsourced images to monitor historic facades |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1186/s40494-022-00664-y |
| Publisher version: | https://doi.org/10.1186/s40494-022-00664-y |
| Language: | English |
| Additional information: | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
| Keywords: | Cultural heritage, Crowdsourced image processing, Deep learning, Structure from Motion, Remote sensing, Dilated convolution |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10143999 |
Archive Staff Only
 |
View Item |
