# Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review
###### Abstract
While several spatial and non-spatial parameters required for detecting and quantifying of DRM-related elements can be extracted directly from RS imagery, many have to be elicited indirectly. Similarly, Indirect measurements are fundamental tools in related research fields, such as environmental science [27]. They have been also used in social and economic studies in the DRM context, e.g., for social vulnerability [28] and business recovery [29] assessments. Proxies have become central in RS-based DRM studies due to the inherent characteristics of several urban elements, such as social and economic activities, which makes it very difficult, if not impossible, to directly observe them using RS data. Proxies use observable features in RS data used to capture and extract information of interest that is not directly visible or measurable, but is correlated with the former. In recent years, using proxies has become the predominant approach to capture implicit information in urban DRM, for both pre- and post-disaster situations [30]. For example, texture is used as a proxy to extract damage to buildings and roads, with irregular texture indicating damaged area. For the purpose of this review, the most relevant components of the disaster risk cycle for use of RS in urban areas are the post-disaster phase proxies focusing on damage and recovery, and the pre-disaster phase proxies measuring vulnerability and resilience. This work was specifically motivated by a number of limitations in the published literature. Firstly, there is inconsistency in terminology: indirect measurements labelled by various terms, such as index [31], indicator [32], and proxy [33]. In this paper, the term proxy is overarching for all such indirect measurements. Secondly, duplications in efforts: in certain cases methodologically the same proxies have been developed independently in more than one domain. For instance, the presence of vegetation in urban areas has been used as a proxy for both social vulnerability [33] and post-disaster recovery [34] assessments. Thirdly, the current DRM literature suggests several proxies that are, at times, unreliable. For example, the presence of vehicles has been used to evaluate the accessibility of the roads [34]. However, the metric is highly dependent on the image acquisition date and time, and other legal or environmental parameters that affect the presence of vehicles. Finally, need to go beyond the physical side towards functional assessments is prominent. Yet, few proxies have been developed for social, economic, and functional assessments in urban DRM. This paper conducts critical analyses on current RS-based proxies and borrows insights from other research fields (e.g., the economy and social sciences). To address these limitations, we conduct a systematical review following a number of steps. We start by splitting reviewed proxies into four groups that characterize built-up, economic, social, and natural environments. Then, a proxy catalog for each group is generated based on the reviewed studies that were used by researchers and governments in the different DRM phases. Finally, a comprehensive analysis is done on the reviewed proxies, and the most suitable proxies for each DRM situation are discussed. These analyses demonstrate current limitations, including lack of proxies to evaluate urban functions for DRM and suggests future directions in developing new proxies. Furthermore, proxies originating from the wider RS domain and other disciplines--urban form studies, structural engineering, and natural sciences--were identified and systemized in this paper. This structured analysis may provide guidance for developing reliable RS-based proxies. The review is structured as follows. Section 2 defines the terminology used for indirect measurements, i.e., proxy, in this article. In Section 3, we explain the methodology employed for this review. Section 4 presents a comprehensive literature review of RS-based proxies for each of the four selected environments, as well as a corresponding table of proxies.