Monitoring Roofs of Traditional Buildings using Remote Sensing

Climate change has caused great damages in traditional buildings in the UK and the world. To ensure that the buildings are maintained properly and safe for occupants to stay, it is necessary to perform monitoring and analyzing maintenance solutions. Traditional roof condition monitoring and analysis rely on manual operation, which is time-consuming, labor-intensive, and unsafe (Curtis & Kennedy, 2016). To overcome these limitations, modern monitoring methods like remote sensing have been developed, such Scan-to-BIM and Scan-vs-BIM applications include condition assessment on buildings, bridges, pipes, tunnels (Koch et al., 2015), monitoring of building settlement (Dai & Lu, 2010) and detecting building roof damage (Vetrivel et al., 2018). Advanced analysis methods like deep learning have also been applied in civil engineering, such as crack detection on concrete structure surface (Yokoyama et al., 2017). However, there is not a complete library containing traditional building roof data of different types and with different deteriorations for research and industry use. Besides, most existing studies of civil engineering on deep learning only focus on recognizing objects like cracks, but few of them are from the perspective of building life-cycle analysis like raising maintenance solutions.