# Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review
## 4 Remote Sensing-Based Proxies for DRM in Urban Areas
### Built-Up RS-Based Proxies
#### 4.1.1 Buildings Category  
The predominant land cover in urban areas is the buildings class. This is why one of the mostly used proxies for damage [37] and recovery [42] assessments in urban DRM is extracting the status  
Figure 1: Number of annual publications on remote sensing-based proxies for Disaster Risk Management (DRM). Papers with a focus on two DRM areas, e.g., both damage and recovery assessments, are counted on both of them separately. Figure 2: Number of developed remote sensing-based proxies for DRM in each environment (Built-up, Economic, Social, Natural). The proxies that are used interchangeably in more than one area, e.g., damage and recovery assessments, are counted on all of the used categories separately. However, the green colored bar shows the total number of unique RS-based proxies for each environment. of the buildings. For example collapsed/damaged or reconstructed buildings are detected either using multi-temporal RS data by comparing the pre-event and post-event situations [43], or only using mono-temporal RS data acquired after the disaster [44]. For example, [PERSON], et al. [25] identify the damage level in urban areas after the Haiti earthquake by extracting buildings damage while using qualitative analysis from satellite imagery along with the integration of visual field data. Similarly, [PERSON], et al. [45] use aerial photography to identify collapsed and damaged buildings (residential houses), and develop a damage pattern mining framework to detect them. Besides, the physical changes in the recovery processes are assessed mostly using a pathway from rubble of collapsed buildings to standing (reconstructed) buildings. Hence, [PERSON], et al. [32] use the reconstruction of buildings as a proxy for recovery assessment after an earthquake. Several studies also employ this proxy for physical recovery assessments [34; 32; 46; 47; 48; 49]. A reconstruction of some specific buildings (e.g., commercial buildings) is also used as a proxy for socio-economic recovery assessments [49]. However, all of them disregard the functional recovery assessment of buildings, which indicates whether a building is operating and/or the building is being used for its intended type of activity (e.g., commercial). In addition, the value of point height change has been used as a proxy to extract information for grading the damage for each building using pre- and post-earthquake DEMs [50]. Although this proxy provides a good source of information to assess per building damages, it cannot detect damages that do not necessarily change the value of the height of the buildings, such as cracks and holes in facades. Some proxies are considered as higher level ones due to their indirect observability in addition to indirect interpretations to indicate something meaningful for computation. One of them is shadow, which has been used in the DRM context to extract information about collapsed/damaged/reconstructed structures from RS images [51; 52; 53; 44]. [PERSON] and [PERSON] [37] provide a comprehensive discussion of pros and cons of using shadow as a proxy to detect building-based changes. They demonstrate that, although missing or smaller shadows compared to pre-event situation may indicate a damaged or collapsed buildings (e.g., pancake collapse), the sun direction differences between the acquired images resulting in shadow changes, can lead to confusion. Image texture has frequently been used as a proxy to identify damaged/collapsed buildings [54; 55; 56; 57], damaged roads [58; 59], and damaged areas [60; 9] from RS imagery. Damaged buildings/areas have more irregular texture than intact ones [55]; therefore, the damaged/collapsed ones can be extracted by comparing their textures. However, this proxy may not be reliable in detecting damages to informal settlement (slums) since they normally have irregular textures. Moreover, texture has been shown as useful additional information for urban structure discriminations, and has been used for building seismic vulnerability analysis [61; 62].