# 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
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Proxy** & **Essence** & **Used in Disaster Phase** & **Used RS Data to Extract** & **Mono** & **Multi** & **Key References** \\ \hline
**Mean flood service height** & Higher mean flood water center noise among changes & Damage & VIR satellite imagery & ✓ & [2, 3] \\
**Sca water penetration on land** & Low level new water penetration on land & Damage & AIDS image & ✓ & ✓ & [2, 3] \\  & human simulation zone & Damage & PIASA data & ✓ & ✓ & [2, 3] \\ \hline
**Surface water/case/Level** & Increase in the desired flood water coverage in land & Damage & VIR satellite images, DEM & ✓ & [2, 3] \\
**Based water coverage** & Increase the damage in the areas & Damage & data, ASTER images & ✓ & [2, 3] \\  & & Identify how far water model inland and extract & Landset ML, VIR serial & ✓ & & [3,10,11,102] \\  & debris zone & Damage & and satellite imagery & ✓ & [3,10,11,102] \\  & & focusful permeability of the surface & Recovery & VIR satellite images & ✓ & [2, 3,44,64, 77, 78] \\  & & & & & & \\ \hline
**Ratio of permanent residential** & To extract movement to reconstructed/recovered areas & Recovery & VIR satellite images & ✓ & ✓ & [2, 3,44,64, 79, 78] \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline
**Database network density** & Lower drainage network density in case of a watershed & Vulnerability & Synthetic imagery, DEM & ✓ & [2,3,44,64, 79, 78] \\  & & disaster increase the vulnerability & & data, VIR satellite images & ✓ & [2,3] \\  & & & & & & \\ \hline
**River network density** & Higher river network density in case of a watershed & Vulnerability & DEM data, & ✓ & [2, 3] \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & \\  & & & & & & \\ \hline
**Inversion surface** & has low level of permeability which increases & Vulnerability & Landset images & ✓ & [2, 3] \\  & & the vulnerability & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Remote sensing-based built-up proxies for urban DRM, Others category. Mono and Multi refer to mono-temporal and multi-temporal RS data that used for extraction, respectively. Several proxies rely on geometric and morphological characteristics of built-up components to extract detailed information about structural deformations of buildings. They are used to compute the damage ratio of buildings. [PERSON] and [PERSON] (1999) use the discontinuity of surface of building rooftops and vertical walls as a proxy for damage detection. They show that continuous roofs indicate intact buildings. In addition, [PERSON], et al. (2017) identify cracks and holes in the building roof or facades, intersections of cracks with load-carrying elements, and dislocated tiles (Figure 3A-C) as proxies to be utilized to assess per-building damages after a disaster. Since these details can only be extracted using VHR images and height data, UAV images were used to produce three-dimensional (3D) point clouds to extract them. The results illustrate the efficiency of using those proxies for building-based damage assessment and classification when UAV data are available. [PERSON] and [PERSON] (2018) used similar proxies: cracks, displaced and collapsed roofing tiles, wall mortar that is somewhat peeled off, or inclined buildings (Figure 3D) to evaluate and classify the building-based damages, e.g., roof tile displacements and inclined buildings are used as proxies for detecting slightly and heavily damaged buildings, respectively. Rubble piles and debris (Figure 3E) are the first features to draw attention and they have been frequently used as proxies for damage detection ([PERSON], 2010; [PERSON], 2011; [PERSON], 2012; [PERSON], 2013), while spalling of buildings (Figure 3E) also is one of the features for heavy damages which has been used for damage detection with UAV ([PERSON], 2011; [PERSON], 2012) and satellite images ([PERSON], 2015).