# Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review
## 3 Methods  
We systemize the published literature based on classification by (i) DRM stage (focus on pre- and post-disaster) zooming into vulnerability, resilience, damage, and recovery assessments practices, and by (ii) the type of environment changes in which a proxy tries to capture. Each is common in DRM literature and fits well the main purpose of this article. Following [20], we differentiate between 4 types of environment: (1) the built-up environment divided into 'buildings', 'transport' and 'others'; (2) the economic environment with'macro, regional and urban economics' and 'logistics' subcategories;(3) the social environment covering'services and infrastructure' and'socio-economic status'; and (4) the natural environment.  
This review is based on a systematic literature search, performed in summer 2018, using several databases (Web of Science, ScienceDirect, SpringerLinkjournals, Taylor&Francis, and Scopus). We select the articles for this review, using the following keywords: indirect, proxy, proxies, index, indices, indicator, and remote sensing, GIS, vector, raster, map, satellite image, aerial image, lidar, UAV, UAS, drone, crowdsourced, ground image, ground photo, and resilience, resilient, adaptive capacity, coping capacity, preparation, prevention, vulnerability, damage, response, impact, rescue, recovery, reconstruction, rehabilitation, and relief. These keywords are selected to address the goal of the review: to provide a systematic categorization of proxies for DRM and resilience, supporting them with some key citations and relevant state-of-the-art examples. Finding relevant papers for this review was difficult, because not necessarily all of the papers use keywords to describe their indirect measurements (proxies). Indeed, with nearly every type of passive RS being _per se_ indirect, every form of processing of such data inherently uses proxies. Furthermore, there are many commonly used proxies (e.g., NDVI for vegetation cover extraction, the number or configuration of buildings to extract information about urban sprawl and presence of slums, nightlights to detect electrification) that researchers accept as natural RS-based measurements, without labeling them as proxies. Hence, there may be additional studies using RS-based proxies that are not included in this review due to different terminology. Moreover, there might be uncertainties in the used terms for the selected pre-and post-disaster situations, which also made it hard to find relevant studies. For example, for post-disaster damage assessment, similar key phrases have been used such as response to a disaster [40], and impacts of a disaster [41]. Naturally, to the best of our knowledge, we have carried the search as comprehensively as possible. Since the purpose of this article is to provide a structured overview of existing approaches with exemplary citations on key studies rather than perform a full coverage of the literature, the core conclusions are not affected. The review covered journal publications, book sections and conference publications that can be retrieved either via the research engines employed or the websites of the main RS conferences. Only English language papers were considered.