# Comparing Three Machine Learning Techniques for Building Extraction from a Digital Surface Model
## 5 Conclusions  
In this paper, we performed an empirical comparison of three different supervised ML-based building detection methods--HOG-SVM, HOG-ELM, and FCN models--on a benchmark high-resolution remotely sensed dataset using only raster DSMs. Two of these methods belong to the classical feature learning and classifier training category--shallow ML--whereas the third is a DL network. We used HOG as a feature descriptor and trained the classifiers (SVM and ELM) for the first two methods using publicly available ISPRS datasets. The same data were employed for fine-tuning the FCN architecture through transfer learning. The high quality of the publicly available data resolved the major problem of correctly labeling the training data, on which depend the predictive skills of the final trained models. The methods are easy to implement, as analogous functions are widely accessible in both proprietary and open source software and programming languages (e.g., MATLAB, Python, R). Our results demonstrate that determining the footprint of buildings from remotely sensed data can produce different results, depending not only on the urban morphology of the context to be surveyed but also on the model choice. The performances of building detection techniques based on shallow ML were affected by the complexity of the urban context considered, in particular by the presence of vegetation and smaller footprints. The FCN-based model has proven to be the most robust and best-performing method for building extraction from high-resolution DSM data. Furthermore, this DL technique can generate accurate building masks without any manually engineered features with high transferability potential. Due to this, the model has the potential to solve similar pixel classification tasks, such as extraction of a different class (e.g., ground surfaces, vegetation, cars) or multi-class segmentation by being re-trained with adequate ground-truth masks and classes number. Using solely a DSM as input data increases portability between urban areas as such data are widely available, continuously improved, and constantly released [88]. Future work could investigate the potentials in the aforementioned multi-class problem domains, i.e., multi-class semantic segmentation of urban areas, as well as a systematic analysis of the impacts on the accuracy of the raster resolution variation for applications in data-poor environments or at a larger scale. To better explore the robustness to urban fabric heterogeneity and geographic transferability, future experiments may also include the application in different kinds of settlements (e.g., informal, regional, and vernacular settlements) that can present different morphological characteristics. A metrics ensemble based on both raster resolution and urban morphology could also lead to a more complete characterization of the advantages and disadvantages of each algorithm. The knowledge of the urban context, analysis of urban objects, color information from orthophotos [34], and additional data could be used to develop novel methods for classification, pre-processing the inputs, or post-processing the outputs. Thus, studies on combinations of different approaches and data to improve the FCN detection accuracy would be worthwhile. Conceptualization, R.A.; methodology, R.A.; software, R.A.; validation, N.M.N. and A.M.; formal analysis, N.M.N. ; investigation, A.M. and N.M.N. ; data curation, R.A.; writing--original draft preparation, N.M.N., A.M., R.A. and A.S.; writing--review and editing, N.M.N., A.M., R.A. and A.S.; visualization, A.M. and N.M.N. ; supervision, R.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.