## 4 Results
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Algorithm** & **smsin** & **smsout** & **Callin** & **Callout** & **Internet** \\ \hline RF (50) & 0.585 \(\pm\) 0.086 & 0.564 \(\pm\) 0.094 & _0.639 \(\pm\) 0.104_ & _0.614 \(\pm\) 0.068_ & 0.556 \(\pm\) 0.064 \\ RF (100) & 0.585 \(\pm\) 0.097 & 0.548 \(\pm\) 0.078 & _0.610 \(\pm\) 0.118_ & 0.581 \(\pm\) 0.081 & 0.568 \(\pm\) 0.101 \\ RF (200) & 0.593 \(\pm\) 0.074 & 0.573 \(\pm\) 0.075 & _0.626 \(\pm\) 0.108_ & _0.606 \(\pm\) 0.098_ & 0.556 \(\pm\) 0.107 \\ RF (250) & _0.610 \(\pm\) 0.072_ & 0.581 \(\pm\) 0.074 & _0.639 \(\pm\) 0.111_ & _0.606 \(\pm\) 0.080_ & 0.556 \(\pm\) 0.102 \\ RF (500) & _0.606 \(\pm\) 0.077_ & 0.577 \(\pm\) 0.080 & **0.647 \(\pm\) 0.110** * & 0.577 \(\pm\) 0.104 & 0.568 \(\pm\) 0.105 \\ RF (750) & 0.581 \(\pm\) 0.086 & 0.581 \(\pm\) 0.080 & _0.634 \(\pm\) 0.100_ & _0.597 \(\pm\) 0.101_ & 0.564 \(\pm\) 0.104 \\ RF (1000) & 0.589 \(\pm\) 0.076 & 0.581 \(\pm\) 0.091 & _0.630 \(\pm\) 0.113_ & _0.610 \(\pm\) 0.088_ & 0.560 \(\pm\) 0.100 \\ RF (1500) & 0.589 \(\pm\) 0.088 & 0.564 \(\pm\) 0.082 & _0.631 \(\pm\) 0.105_ & _0.602 \(\pm\) 0.084_ & 0.552 \(\pm\) 0.101 \\ DL & 0.523 \(\pm\) 0.102 & 0.552 \(\pm\) 0.057 & 0.519 \(\pm\) 0.069 & 0.531 \(\pm\) 0.036 & 0.535 \(\pm\) 0.119 \\ \hline \hline \end{tabular}  
* The best result is marked in bold, the one with (*) represent the best result of all experiments, and the results in italic indicates no significant differences with the best according to the \(t\)-test performed  
\end{table} 
Table 4: Random forests (RF) and deep learning (DL) performance metrics. Figure 3: (**a**) Ground truth and (**b**) classification predictions for the 15 \(\times\) 15 subgrid classified with SVM (polynomial kernel) classification for smsout. Yellow: work, blue: home, red: forest. Geójson.io visualization. Figure 4 shows the kNN classification predictions for the tested subgrids. It shows the smsin results with 10 neighbors and CI distance, as it provided the highest accuracy in the tests. In this case, more work cells were detected, but several errors were made as they marked several forest and isolated areas as workplaces. The same happened with the home and forest distinction; it did not correctly classify the mountainous area of _La Marzola_ as forest. Figure 5 shows the RF classification predictions for the tested subgrids. It shows the callin results with 500 estimators, as it provided the highest accuracy in the tests. In this case, the areas classified as workplaces were much better defined, although some errors were made by not identifying the airport area as work and two isolated cells marked as work incorrectly. For home and forest detection, it was much better in this classification compared to the previous methods because it did a better job at identifying forest areas, and the errors were less notorious here. The mistakes in classification can be understood because of the topology of the Trento province and RBS cell coverage, which can be low in isolated areas and thus explains some of the errors. Figure 4: (**a**) Ground truth and (**b**) classification predictions for the 15 \(\times\) 15 subgrid classified with kNN (10 neighbors and CI distance) classification for smsin. Yellow: work, blue: home, red: forest. Geojson.io visualization. Figure 5: (**a**) Ground truth and (**b**) classification predictions for the 15 \(\times\) 15 subgrid classified with RF classification (500 estimators) for callin. Yellow: work, blue: home, red: forest. Geojson.io visualization. Figure 6 shows the DL classification predictions for the tested subgrid. It shows the smsout results since they achieved the highest accuracy in the tests. The work label was not very precisely detected here, failing to properly detect the airport area and the industrial zone in Gardolo. The forest and home areas were also not very well delimited in most of the mountainous areas.