This research demonstrates how data collected by citizen scientists can act as a valuable resource for heritage managers. It establishes to what extent visitors’ photographs can be used to assist in aspects of condition monitoring focusing on biological and plant growth, erosion, stone/mortar movement, water ingress/pooling and antisocial behaviour.
This thesis describes the methodology and outcomes of Monument Monitor (MM), a project set up in collaboration with Historic Environment Scotland (HES) that requested visitors at selected Scottish heritage sites to submit photographs of their visit. Across twenty case study sites participants were asked to record evidence of a variety of conservation issues. Patterns of contributions to the project are presented alongside key stakeholder feedback, which show how MM was received and where data collection excelled. Alongside this, the software built to manage and sort submissions is presented as a scalable methodology for the collection of citizen generated data of heritage sites.
To demonstrate the applicability of citizen generated data for in depth monitoring and analysis, an environmental model is created using the submissions from one case study which predicts the effect of the changing climate at the site between 1980 - 2080. Machine Learning (ML) is used to analyse submitted data in both classification and segmentation tasks. This application demonstrates the validity of utilising ML tools to assist in the analysis and categorising of volunteer submitted photographs.
The outcome of this PhD is a scalable methodology with which conservation staff can use visitor submitted images as an evidence-base to support them in the management of heritage sites
