# A New Scene Sensing Model Based on Multi-Source Data from Smartphones
## 3 Results
### Ablation Experiment  
In this section, ablation experiments are conducted to compare four different network configurations, as shown in Figure 14: CNN1, CNN1 + CNN2, CNN1 + BiLSTM, and CNN1 + CNN2 + BiLSTM. Each network is trained ten times to obtain the average results, which are presented in Table 5. The test results indicate that the classification accuracy of a single CNN network is 90.28%. In comparison, the feature accuracy of the dual-scale CNNnetwork improves to 95.06% due to its enhanced feature extraction capabilities, combining both local and global features.  
Furthermore, when comparing to the single CNN network, the accuracy of the BiLSTM network rises to 96.91%, benefiting from its superior ability to capture global and spatial features. By combining the dual-scale CNN and BiLSTM networks, the overall accuracy increases to 97.83%. For combined accuracy and recall, the F1 metric is employed, showing that the average F1 value for all categories rises to 0.9731 with the combination of the dual-scale CNN and BiLSTM networks. This represents an improvement compared to 0.8964 for the single CNN, 0.9481 for the dual-scale CNN (CNN1 + CNN2), and 0.9675 for the CNN1 + BiLSTM configuration, which also shows a corresponding increase.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** &
\begin{tabular}{c} **Total Number** \\ **of Scenarios** \\ \end{tabular} & **Data Volume** & **Municipalities** & **Scenario Categories Involved** \\ \hline Training set & 217 & 105,164 & Beijing, Guangzhou, Hangzhou & Indoor, semi-indoor, outdoor, semi-outdoor \\ Validation set & 34 & 29,178 & Beijing, Guangzhou, Hangzhou & Indoor, semi-indoor, outdoor, semi-outdoor \\ Test set & 71 & 48,536 & Beijing, Shanghai & Indoor, semi-indoor, outdoor, semi-outdoor \\ \hline \hline \end{tabular}
\end{table}
Table 4: Distribution of datasets.  
Figure 14: Structure of the ablation experiment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **CNN1** & **CNN1 + CNN2** & **CNN1 + BiLSTM** & **CNN1 + CNN2 + BiLSTM** \\ \hline Accuracy (\%) & 90.28 & 95.06 & 96.91 & 98.25 \\ Macro-F1 & 0.8964 & 0.9481 & 0.9675 & 0.9810 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of ablation experiments.  
Figure 15a presents the confusion matrix for the combined classification of the dual-scale CNN network and the BiLSTM network. For the data labeled with the indoor category, 98.3% of the data are classified correctly, while 1.2% are misclassified as semi-indoor and 0.5% as semi-outdoor. This misclassification can be attributed to the similarity in features between the semi-indoor, semi-outdoor, and indoor scenes.  
For the data labeled as semi-indoor, 97.6% are correctly classified while 1.7% are incorrectly identified as indoor and 0.7% as semi-outdoor. This indicates that the features of semi-indoor scenes are closer to those of indoor scenes, but there is still a notable chance of misclassifying them as semi-outdoor venues.  
Similarly, for the data labeled as semi-outdoor, 97.8% are classified correctly, while 0.8% are misclassified as semi-indoor and 1.4% as outdoor. This suggests that although the semi-outdoor scene may experience some occlusion, the signal is not significantly affected in many environments. Lastly, 99.3% of the outdoor scenes are classified correctly, which is due to the more distinct characteristics of outdoor scenes, resulting in the highest classification accuracy among the four categories.