# Historic Built Environment Assessment and Management by Deep Learning Techniques: A Scoping Review
## 3 Results  
### Building Damage Classification  
The first significant aspect emerging from the literature analysis is the application of DL techniques for assessing historic buildings' conservation status, focusing on the identification and classification of surface deterioration of facades and masonries. In this context, the relevant features extracted by the algorithms and the different classification methodologies adopted to perform their respective tasks are highlighted (Table 2).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Clusters** & **Keywords** \\ \hline \multirow{3}{*}{Cluster 1} & Convolutional Neural Network; Learning algorithms; \\  & Buildings; Damage detection; \\  & Deterioration; Historic preservation \\ \hline \multirow{3}{*}{Cluster 2} & Convolutional Neural Network; \\  & Building damage; Damage assessment; \\  & Disasters; Natural disasters; \\  & Remote sensing; Antennas \\ \hline \multirow{3}{*}{Cluster 3} & Architecture; Learning models; \\  & Crack detection; Object detection; \\  & Image classification; Image segmentation; \\  & Structural health monitoring \\ \hline \multirow{3}{*}{Cluster 4} & Deep learning; Machine learning; \\  & Classification; Learning systems; \\ \cline{1-1}  & Cultural heritage; Heritage structures \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cluster classification of author keywords and indexed keywords. VOSviewer.  
\begin{table}
\begin{tabular}{c c c c c c c} \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} & **Damage Classes** \\ \hline BINARY CLASSIFICATION & & & & & & \\ \hline \multirow{3}{*}{[30]} & Mask R-CNN & & 74-80(@0.5) * & & & moist area, \\  & (ResNet101 + FPN + RPN) & & 17–26(@0.5) * & & & biological colonization \\ \hline \multirow{3}{*}{[31]} & CNN (VGG16) & 98.92 & 94.50 & 88.70 & 91.40 & \begin{tabular}{c}erosion, material loss, \\ color change, \\ natural, sabotage \\ \end{tabular} \\ \hline \multirow{3}{*}{[32]} & CNN--Grad-CAM & & & & & & \\  & (EfficientNetB0) & 96.50 & 96.73 & 96.25 & 96.49 & \begin{tabular}{c} cracks, exfoliation, \\ deterioration \\ \end{tabular} \\ \hline \multirow{3}{*}{[33]} & CNN (EfficientNetB0) & 99.61 & & 99.97 & \
\begin{tabular}{c} roof tilts inclinations \\ \end{tabular} \\ \cline{1-1} \cline{2-6}  & Mask R-CNN & & 97.50(@0.5) * & & tiles damage \\  & (ResNet101 + FPN + RPN) & & & & & \\ \hline \multirow{3}{*}{[35]} & CNN & 97.73 & & & & \\ \cline{2-6}  & MULTI-CLASS CLASSIFICATION & & & & & \\ \hline \multirow{3}{*}{[31]} & CNN (VGG16) & 96.26 & 94.80 & 94 & 76 &
\begin{tabular}{c}erosion, material loss, \\ color change, \\ natural, sabotage \\ \end{tabular} \\ \hline \multirow{3}{*}{[36]} & CNN & 96.40 & 96.40–100 & & & & \\  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Review of image-based DL models applied for building damage classification on HBE, with accuracy values obtained.