# Historic Built Environment Assessment and Management by Deep Learning Techniques: A Scoping Review
## 3 Results
### Building Damage Classification
#### 3.1.1 Binary Classification
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & \begin{tabular}{c} **DL Algorithm** \\ **(Architecture)** \\ \end{tabular} & \begin{tabular}{c} **Accuracy** \\ **mPA [\%]** \\ \end{tabular} & \begin{tabular}{c} **Precision** \\ **mAP [\%]** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ **[\%]** \\ \end{tabular} & \begin{tabular}{c} **F1-Score** \\ **[\%]** \\ \end{tabular} & \
\begin{tabular}{c} **IoU/** \\ **mIoU** \\ **[\%]** \\ \end{tabular} &
\begin{tabular}{c} **Damage Classes** \\ **[\%]** \\ \end{tabular} \\ \hline
[37] & \
\begin{tabular}{c} Faster R-CNN + RPN \\ (InceptionV2) \\ \end{tabular} & & & & 94.60 &
\begin{tabular}{c} crack, loss, \\ detachment, biological \\ colonization \\ \end{tabular} \\ \hline
[38] & \begin{tabular}{c} CNN--Grad-CAM \\ (MobileNetV3 Small) \\ \end{tabular} & 63.46 & 69.97 & 55.42 & \begin{tabular}{c} cracks, corroded steel \\ reinforcements, \\ deteriorated concrete, \\ honeycombs, moisture \\ spots, shrinkage cracks \\ \end{tabular} & 63.46 & 69.97 & 55.42 & \
\begin{tabular}{c} cracks, corroded steel \\ reinforcements, \\ deteriorated concrete, \\ honeycombs, moisture \\ spots, shrinkage cracks \\ \end{tabular} & 69.06 &
\begin{tabular}{c} crust, detachment, \\ cracks, oxidation, \\ humidity, efflorescence \\ \end{tabular} \\ \hline
[39] & \
\begin{tabular}{c} CNN \\ \end{tabular} & 85.60 & 85.60 &
\begin{tabular}{c} damage levels \\ \end{tabular} \\ \hline MULTIT-LABEL CLASSIFICATION & & & & & & \\ \hline
[41] & \begin{tabular}{c} Mask R-CNN \\ (ResNet101 + FPN + RPN) \\ \end{tabular} & 53–100 & & & & \
\begin{tabular}{c} biological colonization, \\ contour scaling, crack, \\ higher plant, impact \\ damage, microkars, \\ missing part \\ \end{tabular} & 53–100 & & & &
\begin{tabular}{c} outdoor scaling, crack, \\ higher plant, impact \\ damage, microkars, \\ missing part \\ \end{tabular} \\ \hline
[42] & \
\begin{tabular}{c} CNN (MobileNetV2) \\ \end{tabular} & 86.80 & 84 & & &
\begin{tabular}{c} efflorescence, spall, \\ crack, mold \\ \end{tabular} \\ \hline
[43] & \
\begin{tabular}{c} YOLOv5 \\ \end{tabular} & 93.70(@0.5) * & 91.80 & & &
\begin{tabular}{c} crack, discoloration, \\ exposed bricks, \\ spalling \\ \end{tabular} \\ \hline
[44] & \
\begin{tabular}{c} Faster R-CNN + RPN \\ (ResNet101) \\ \end{tabular} & 95 & & & &
\begin{tabular}{c} efflorescence, spalling \\ \end{tabular} \\ \hline
[45] &
\begin{tabular}{c} CNN (DeepLabV3+ \\ (backbone ResNet101) \\ \end{tabular} & 84.40 & & 74.30 & 66.90 & vegetation \\ \hline
[46] & \
\begin{tabular}{c} Faster R-CNN + RPN \\ (ResNet101 + FPN) \\ \end{tabular} & 58.19(@0.5) * & & & &
\begin{tabular}{c} cracks, spalling \\ \end{tabular} \\ \hline
[47] &
\begin{tabular}{c} YOLOv5x \\ \end{tabular} & 81(@0.5) * & 85 & & spalling \\ \hline OBJECT SEGMENTATION & & & & & \\ \hline \multicolumn{3}{p{113.8 pt}}{edge-enhanced} & \multicolumn{3}{p{113.8 pt}}{} & \multicolumn{3}{p{113.8 pt}}{} & \multicolumn{3}{p{113.8 pt}}{} & \multicolumn{3}{p{113.8 pt}}{} \\
[48] &
\begin{tabular}{c} edge-enhanced \\ Mask R-CNN \\ (ResNet101) \\ \end{tabular} & 93.23(@0.5) * & 84.21 & missing parts \\ \hline \hline \end{tabular}  
* Value obtained at IoU = 0.5.  
\end{table} 
Table 2: _Cont._Residual Network and the Feature Pyramid Network (ResNet101 + FPN) were chosen as convolutional layers because of their efficiency and celerity. FPN was applied after ResNet101 and exploited a pyramidal structure to extract multiple features at different scales. Moreover, a Region Proposal Network (RPN) was applied to the input feature maps, which is a neural network that works by proposing probable regions, which likely contain objects of interest, using a sliding-window technique. Regarding moist areas, the model provides highly accurate outcomes (mean average precision in the testing phase varying from 74% to 80%), unlike the predictions for biological colonization, which are less accurate. Similarly, [PERSON] et al.