# People Detection Using Artificial Intelligence with Panchromatic Satellite Images
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Limit (cm)** & **TP** & **FP** & **FN** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline \multirow{4}{*}{Faster R-CNN without mask} & 15 & 10 & 177 & 204 & 5.3 & 4.7 & 5 \\  & 30 & 40 & 147 & 174 & 21.4 & 18.7 & 20 \\ \cline{1-1}  & 45 & 58 & 129 & 156 & 31 & 27.1 & 28.9 \\ \cline{1-1}  & 60 & 68 & 119 & 146 & 36.4 & 31.8 & 33.9 \\ \hline \multirow{4}{*}{Faster R-CNN with mask} & 15 & 11 & 110 & 203 & 9.1 & 5.1 & 6.6 \\ \cline{1-1}  & 30 & 34 & 87 & 180 & 28.1 & 15.9 & 20.3 \\ \cline{1-1}  & 45 & 54 & 67 & 160 & 44.6 & 25.2 & 32.3 \\ \cline{1-1}  & 60 & 67 & 54 & 147 & 55.4 & 31.3 & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of people detection without data augmentation but with a 15 cm buffer. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Limit (cm)** & **TP** & **FP** & **FN** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline \multirow{4}{*}{Faster R-CNN without mask} & 15 & 20 & 279 & 194 & 6.7 & 9.3 & 7.8 \\  & 30 & 72 & 227 & 142 & 24.1 & 33.6 & 28.1 \\ \cline{1-1}  & 45 & 107 & 192 & 107 & 35.8 & 50 & 41.7 \\ \cline{1-1}  & 60 & 121 & 178 & 93 & 40.5 & 56.5 & 47.2 \\ \hline \multirow{4}{*}{Faster R-CNN with mask} & 15 & 17 & 219 & 197 & 7.2 & 7.9 & 7.6 \\ \cline{1-1}  & 30 & 70 & 166 & 144 & 29.7 & 32.7 & 31.1 \\ \cline{1-1}  & 45 & 101 & 135 & 113 & 42.8 & 47.2 & 44.9 \\ \cline{1-1}  & 60 & 116 & 120 & 98 & 49.2 & 54.2 & 51.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Detection with data augmentation but without a 15 cm buffer. Figure 8 documents the results of Table 7, where no mask was used. False positives occurred in areas where the surface appeared lighter due to specular reflection. Additionally, there were various transitions between surfaces where pixels could be misinterpreted as representing people. This issue was particularly evident on rooftops and sidewalks (Figure 8), where the transitions between different surface textures and lighting conditions led to pixel patterns resembling those of human figures, resulting in incorrect detections on the satellite images. This can also be seen in Table 9, where precisely such surface types have a higher density of predicted data than ground truth data (Locations 6 and 8). Additionally, areas where individuals were in close proximity (Location 7 in Table 10) exhibited an increased incidence of false negatives. In these situations, the model struggled to accurately classify individuals, resulting in missed detections. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Location** & **Ground Truth** & **Predicted** \\ \hline
6—Prague Castle—surface 1 & 0.014 & 0.037 \\
7—Prague Castle—surface 2 & 0.034 & 0.027 \\
8—Prague Castle—surface 3 & 0.009 & 0.014 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The population density calculated for ground truth and predicted data per 1 m\({}^{2}\). Figure 7: Results of the model including data augmentation and a buffer. Red indicates pixels with people (ground truth), and blue the most probable detected locations with a mask. Location 7 (pixel resolution is 30 \(\times\) 30 cm). Figure 9 clearly demonstrates that the combination of data augmentation and a buffer with no mask resulted in the highest number of true positive people, with almost 64% detection. Data augmentation and buffers significantly increase the number of TPs, while masking has a less significant effect.