# Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data
## 4 Evaluation
### Evaluation of Reference Pressure Extraction  
The reference pressure value for the altitude estimation with the barometric formula corresponds to the location that follows the entrance of a building, as detailed described in Section 4.3. As a result, the identification of the OITransition is necessary in order to identify the building entrance. The transition is identified by monitoring peaks and drops by monitoring peaks and drops in the  
Figure 12: Data collected from an iPhone 7 and a Samsung Galaxy S6, while the user had climbed three floors upwards and the same number of floors downwards. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Fast**} & \multicolumn{2}{c}{**Normal**} & \multicolumn{2}{c}{**Slow**} \\ \cline{2-7}  & **Floors** & **Stairs** & **Floors** & **Stairs** & **Floors** & **Stairs** \\ \hline Floors & 1584 & 58 & 2037 & 0 & 2683 & 0 \\ Stairs & 179 & 296 & 76 & 404 & 157 & 472 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix of stair removal. Figure 13: Dataset used for the evaluation of the stair removal method. The data was collected from the same route for three different visits and walking velocities, approximately 1\(\times\), 1.5\(\times\) and 2\(\times\)[15]. readings of a number of sensors and their fusion, as suggested by [22] and described in Section 4.3. However, in our scenario, the ambient light, the GPS uncertainty and the disturbances of the magnetic field are taken into consideration, rather than the WiFi Received Signal Strength (RSS) and the Global System for Mobile Communications (GSM) RSS. The approach has been evaluated in three different buildings with four collected datasets for each building, during day and night. Our collected data and the algorithm used for the evaluation are open-source and can be found in [20]. As can be seen in Figure 14, the OITransition (red dots) was successfully identified in all of our datasets. Additionally, the entrance location could also be approximately determined by our approach. This was considered as the place of the transition, for example, the space between the last low GPS uncertainty values and the first high GPS uncertainty values. As a result, we could additionally estimate the spatial error of our approach for the OITransition identification. Hence, the entrance location latitude has been approximated by an average of 1.6 m, while the entrance longitude has been approximated by an average of 5.5 m. This score was lower than the GPS average error outdoors, which was between 10 and 12 m.  
Furthermore, five out of nine times, the entrance location was identified at the latitude of 48.1489, while two times it was identified at the latitude of 48.14895 and once it was identified at the latitudes of 48.14885 and 48.149. The final latitude was to be decided on the basis of the median, which was 48.14894251, while the true entrance latitude as mapped in the open street maps was 48.1489277. Hence, our algorithm scored an error of 0.00001\({}^{\circ}\), which corresponded to less than 1.64 m. Regarding the longitude, three out of nine times the entrance was localized at the longitude 11.5677, twice at 11.56775 and once at 11.5675, 11.5676, 11.56765 and 11.568. The final entrance location was estimated from the median at 11.568, when the true entrance was located at longitude 11.568. As a result, our algorithm had an error of 0.00004\({}^{\circ}\), which corresponded to approximately 4.614 m.  
Finally, in Tables 4-6, a detailed evaluation of the OITransition determination for each sensor and the sensor fusion for all 3 buildings and 12 datasets is presented. According to the tables, our algorithm scored an average of 96.8% for precision, 94.2% for recall and 95.5% for the F-score, for identifying the OITransition using a GPS sensor. It scored 93.6% for precision, 96.3% for recall and 94.9% for the F-score for OITransition detection with a light sensor. It scored 88.8% for precision, 89.2% for recall and 89% for the F-score for OITransition detection with a magnetic sensor.