Monitoring Roofs of Traditional Buildings using Remote Sensing

气候变化对英国和世界的传统建筑造成了巨大的破坏。为了确保建筑物得到适当的维护和居住者的安全，有必要进行监测和分析维护解决方案。传统的顶板状态监测和分析依赖于人工操作，耗时，劳动密集且不安全（Curtis & Kennedy，2016）。为了克服这些限制，已经开发了现代监测方法，如遥感，这种Scan-to-BIM和Scan-vs-BIM应用包括对建筑物、桥梁、管道、隧道的状况评估（Koch等人，2015）、监测建筑物沉降（Dai & Lu，2010）和检测建筑物屋顶损坏（Vetrivel等人，2018年）。像深度学习这样的高级分析方法也已应用于土木工程，例如混凝土结构表面的裂缝检测（Yokoyama et al.，2017年）的报告。然而，没有一个完整的图书馆包含不同类型的传统建筑屋顶数据，并具有不同的恶化，以供研究和工业使用。此外，现有的土木工程深度学习研究大多只关注裂缝等物体的识别，而很少从建筑物生命周期分析的角度来提出维护解决方案。

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.