# Comparing Three Machine Learning Techniques for Building Extraction from a Digital Surface Model
## 4 Discussion  
The study compared the performance of three common supervised ML techniques--HOG with SVM, HOG with ELM, and FCN--for the task of building footprint extraction from high-resolution DSM data on a benchmark dataset of DSMs of three different urban contexts (Vaihingen an der Enz, Potsdam, and Toronto). Our results showed that all the ML techniques could successfully complete the task, but the FCN appeared more robust to urban fabric diversity.  
The performance of both HOG-based models was influenced by the urban context investigated and in particular decreased within the Vaihingen area. This area is more challenging due to the complexity of the urban fabrics that are composed of many small buildings varying in shape, sparse low vegetation and trees, and irregular narrow road networks. The improvements in the Potsdam area could be attributed to the larger footprints of the constructions and the relatively reduced number of trees. The worst generalization ability of the two methods based on classical feature learning and classifier training may be caused by the size of the training dataset [72, 85], confirming the worse performance of the ELM classifier compared to the SVM on small datasets [72].  
In summary, these results show that a DL approach based on FCNs is the most preferable method as it achieves good classification regardless of the urban context, despite the inaccuracy in contours and boundaries that is a drawback of DL-based segmentation [86], and support evidence from previous studies [35, 36, 38, 39, 41, 85]. Furthermore, such an approach automates the feature engineering and benefits from transfer learning that limits the impacts of the training data size.  
However, with a limited data size, although sufficient for the comparison, caution must be applied, as the findings might not be fully appliable in the case of large datasets or urban scenarios with substantially different morphological structures, e.g., Asian cities [87].  
Table 2 summarizes the main advantages and disadvantages of the three methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Algorithm** & \multicolumn{2}{c}{**Advantages**} & \multicolumn{2}{c}{**Disadvantages**} \\ \hline  & & & & - & Training data size sensitivity \\  & & & & - & Manual feature engineering \\ HOG-SVM & [60] & - & Good predictive abilities & - & Not suitable for urban context \\  & & & & & with sparse vegetation, small \\  & & & & & footprints, and narrow roads \\ \hline  & & & & - & Possible training data size \\  & & & & & sensitivity \\ HOG-ELM & [70] & - & Average predictive abilities & - & Manual feature engineering \\  & & - & Fastest computational time & - & Not suitable for urban context \\  & & & - & Fastest computational time & - \\  & & & & - & Not suitable for urban context \\  & & & & & with large footprints \\ \hline  & & & - & Best predictive abilities in all \\  & & & urban contexts & \\  & & - & Transfer learning (training data \\ FCN & [50] & & size robustness) & - & Irregular footprint edges \\  & & - & Automatic feature extraction & - & Irrelevant feature susceptibility \\  & & - & Easy implementation & \\  & & - & High transferability potential & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Advantages and disadvantages of the three implemented methods.