Managing subsidence and its impacts on cities in coastal and delta areas is a global challenge that requires comprehensive risk reduction policies, including both mitigation and prevention strategies. Urban areas often lack systematic methodologies for determining appropriate countermeasures. This paper proposes a twofold strategy for selecting subsidence reduction measures in urban areas – which refer to structural (i.e. technical) measures to prevent and mitigate subsidence and its physical consequences – based on their applicability and performance. The question-and-response (Q&R) system serves as a decision tree to identify suitable subsidence countermeasures based on their applicability to specific cases. Four indicators of effectiveness – i.e. reduction potential, operational reliability, negative impact, and service life – are then used to assess the performance of subsidence reduction measures. The proposed procedure was applied to 49 cases derived from a review of 52 scientific publications and additional expert sessions and surveys involving 5 academic scholars and 13 experts. Also, the method was applied to examples from Shanghai (China), Jakarta (Indonesia), and the San Joaquin Valley (USA, California). The strategies proposed in this paper proved suitable for an initial screening of subsidence reduction measures applicable in different urban areas, after which a site-specific assessment can follow. Furthermore, this study shows the need to collect and share experiences in evaluating the performance of subsidence reduction measures more systematically and gives a first framework to do so.

In landslide-affected areas, road pavement damage might be significant. The management of road infrastructure requires objective methods for characterizing the condition of the road pavement. Unmanned Aerial Vehicle (UAV) platforms with integrated digital cameras can provide high-resolution images for constructing 3D models of the road. These can be processed to detect and assess the severity of cracks. This chapter aims to present the use of UAV imagery for obtaining quantitative pavement damage information, providing a panorama of the techniques that are currently used. Moreover, it describes a procedure for the processing of the images that provide (i) a damage hotspot map indicating the unevenness of road pavement, (ii) a 3D point cloud with damage/nondamage points and their roughness, and (iii) damage locations classified according to their severity. Last, this Chapter discusses best practices for using a digital camera integrated into a UAV platform for damage assessment and characterization.