# Changes in the Building Stock of Da Nang between 2015 and 2017
## 2 Methods
### Data Processing and Change Detection  
The delineation of built-up areas and the extraction of building heights from the Pleiades image of 2015 was conducted using an object-based image analysis approach, as described by [PERSON] et al. [4]. As described in that study, built-up areas were identified using a multiresolution segmentation. This segmentation was based on the four spectral bands of the acquisition, their three principal components, a Canny edge detection algorithm [13], and a digital surface model retrieved from the tri-stereoscopic information of the Pleiades product [14]. The image objects were then classified into \"built-up\" and \"non-built-up\" using rule-based thresholds applied to the derived object features, resulting in an accuracy of 88.4%. All built-up areas were divided into single buildings using spatial intersection with cadastral boundaries and visual identification, as described by [PERSON] et al. (2019) [3]. The geometric accuracy of single buildings was assessed by the manual digitization of 50 randomly selected \begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Building Type and Statistics** & **Description** & **Representative Reference Picture** \\ \hline \multirow{4}{*}{7} & \multicolumn{2}{c}{**Special structure/other**} \\  & \multicolumn{2}{c}{(\(n=11\))} & \\  & average (standard deviation) of: & \\  & height 5.4 m (5.0 m) & \\  & size1: 552.1 m2 (928 m2) & \\  & length1: 39.3 m (24.0 m) & \\  & width1: 22.1 m (14.8 m) & \\  & persons/household3: - & \\ \hline \multirow{4}{*}{8} & \multicolumn{2}{c}{**Hall** (\(n=17\))} \\  & average (standard deviation) of: & \\  & height 5.1 m (3.8 m) & \\  & size1: 917.6 m2 & \\  & (1654.0 m2)length1: 44.3 m (31.4 m) & \\  & width1: 22.8 m (15.8 m) & \\  & persons/household3: - & \\ \hline \multirow{4}{*}{9} & \multicolumn{2}{c}{**Outbuilding/shock** (\(n=16\))} \\  & average (standard deviation) of: & \\  & height 2.3 m (2.7 m) & \\  & size1: 94.0 m2 (144.8 m2) & \\  & length1: 16.8 m (17.0 m) & \\  & width1: 7.9 m (4.6 m) & \\  & persons/household3: - & \\ \hline \hline \end{tabular}  
* as retrieved from the footprints of the surveyed buildings as described in Section 2.3; 2 as retrieved from the data collection conducted by [PERSON] et al. (2019) [3]; 3 not assessed.  
\end{table} 
Table 1: Cont. buildings, whose areas were then compared to the automatically derived building footprints. The comparison resulted in an overall agreement of 87.2%. While accuracies of over 95% were achieved for buildings of average sizes (50-160 m\({}^{2}\)), larger underestimations were observed for very small (<50 m\({}^{2}\)) and very large (>200 m\({}^{2}\)) buildings, which only make up a small fraction of the buildings in Da Nang. In the end, the derived footprints resulted in building areas which were found to be reliable enough to serve as one of the variables for the classification of building types in the next step. Accordingly, building types were assigned based on the criteria given in Table 1 and manual refinement. This classification typology resulted in an overall number of 241,217 building features in the study area for the year 2015. To understand the quality of the building type classification, an accuracy assessment was conducted based on the 803 building references which were collected during the field campaign, as described in Section 2.3[3]. The confusion matrix between the reference data and the classified building types is shown in Table 1 in the Appendix A and discussed in more detail in Section 3.5. An overall classification accuracy of 90.9% was achieved. In the next step, a typology of changes was developed, consisting of five classes (Table 2): no change (1), construction of a new building (2), changes in the building, e.g., roof color or size, while maintaining the building type according to Table 1 (3), upgrading of the building to a new building type, mostly of higher quality (4), and demolition of the building (5).