## 3 Data Description
### Accuracy Assessment and Known Limitations  
As explained in the Methods section, the number and the geometries of a large proportion of \"Basic\" buildings is the result of a modelling approach based on the statistics of manually digitized building footprints. The geometries of all buildings result from the combination of OBIA segments, automatic generalization, and eventual manual revision or complete manual digitizing. The geometries are therefore only an approximation of the real-world building footprints.  
Using the reference information of the field campaigns, an accuracy assessment of the building type classification of 2015 was applied. It shows that, on the one hand, a high overall accuracy could be achieved, but on the other hand, even with an extensive manual revision of the dataset, there are differences between on-the-ground validation and remote sensing image interpretation (Table 5).  
An accuracy assessment was also conducted for the adjusted building heights (in the knowledge that the heights were adjusted by thresholds derived of the reference dataset). The analysis gives some background on the derivations that have to be expected with the dataset (Table 6).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Building**} & **No. of** & **No. of** & **Change of** & **Area of Buildings** & **Area of Buildings** & **Total Area** & **Area** & **Area** \\  & **Buildings** & **Buildings** & **Number of** & **in \%** & **2008–2009 (m\({}^{2}\))** & **Buildings** & **Boundings** & **Change (m\({}^{2}\))** & **Change (m\({}^{2}\))** & **Change (m\({}^{2}\))** \\ \hline Basic & 135,577 & 170,805 & 35,228 & 26.0\% & 9,590,293 & 11,558,170 & 1,967,877 & 20.5\% \\ Block & 1609 & 1587 & -22 & -14.4\% & 399,830 & 340,747 & 917 & 0.3\% \\ Bungalow & 20,313 & 27,610 & 7297 & 35.9\% & 3853,114 & 5,79,569 & 1,326,455 & 34.4\% \\ Villa & 1542 & 4781 & 3293 & 210.1\% & 471,511 & 1,371,587 & 900,046 & 190.9\% \\ Mid-rise & 503 & 806 & 303 & 60.2\% & 290,089 & 470,566 & 180,477 & 62.2\% \\ High-rise & 211 & 371 & 160 & 75.8\% & 154,731 & 300,134 & 148,403 & 95.9\% \\ Hall & 3396 & 4306 & 910 & 26.8\% & 1,896,004 & 2,456,091 & 560,087 & 29.5\% \\ Special & 211 & 253 & 42 & 19.9\% & 48,338 & 61,508 & 13,170 & 27.2\% \\ Construction & 2263 & 939 & \(-\)1324 & \(-\)58.8\% & 611,744 & 311,927 & \(-\)299,817 & \(-\)49.0\% \\ SUM & 165,625 & 211,458 & 45,833 & 27.7\% & 17,255,654 & 22,053,269 & 4,797,615 & 27.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Basic results derived from the dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & \multicolumn{6}{c}{**Classified Building Types**} & & **Producer’s Accuracy** \\ \cline{3-10}  & & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline \multirow{4}{*}{**Courtesy**} & **1** & 330 & 1 & 3 & 0 & 0 & 0 & 0 & 0 & 99\% \\  & **2** & 0 & 84 & 0 & 0 & 0 & 0 & 0 & 0 & 100\% \\  & **3** & 11 & 0 & 243 & 2 & 0 & 0 & 1 & 0 & 95\% \\  & **4** & 0 & 0 & 11 & 90 & 0 & 0 & 0 & 0 & 89\% \\  & **5** & 0 & 0 & 1 & 0 & 24 & 2 & 2 & 0 & 83\% \\  & **6** & 0 & 0 & 0 & 0 & 1 & 37 & 0 & 0 & 97\% \\  & **7** & 1 & 0 & 2 & 0 & 1 & 0 & 38 & 0 & 90\% \\  & **8** & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 2 & 50\% \\ \hline \multirow{4}{*}{**Courtesy**} & **User’s Accuracy** & 96\% & 98\% & 93\% & 98\% & 92\% & 95\% & 93\% & 100\% &
\begin{tabular}{c} **Overall Accuracy** \\ **848/889 = 95.4\%** \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy assessment of the building type classification 2015. No information on construction sites (class 9) was collected for the reference data.