# People Detection Using Artificial Intelligence with Panchromatic Satellite Images
## 4 Discussion  
The results in Tables 6-9 show the success of detecting people from satellite images. The F1 score results are approximately 30% when the model is trained without the use of data augmentation. The utilization of a 15 cm buffer leads to better results in terms of the  
Figure 8: Results of detection with data augmentation and a 15 cm buffer but without a mask. Red indicates pixels with people (ground truth), and blue the most probable detected locations (pixel resolution is 30 \(\times\) 30 cm).  
Figure 9: Correctly detected people (recall) with various methods and preprocessing.  
F1 score, and models trained on the dataset that employed data augmentation achieve an F1 score exceeding 50%.  
Figure 10 shows people detection without using data augmentation compared with using data augmentation. It can also be seen from Figure 9 that data augmentation helped to detect more people. However, without the use of a mask, Faster R-CNN was able to detect more people than with the use of a mask.  
Figure 11 shows Faster R-CNN detection using a 15 cm buffer and data augmentation. More people were detected with the buffer than without (Figure 9). With the mask, two additional people were falsely detected, but without the mask two people were missing. It can be seen from Figure 10 that, again, data augmentation improves the results of people detection, and that when using a buffer and data augmentation all the people in Figure 11 were detected with no FPs.  
The maximum achieved F1 score was 54.5%, which is not satisfactory. [PERSON] and [PERSON] [20] also applied Faster R-CNN for processing WV3 and WV4 images, and they obtained an F2 score of 77.8% in heterogeneous areas and 73% in homogeneous areas. Their higher accuracy may be due to the larger pixel representation of an elephant than a human. [PERSON], [PERSON], and [PERSON] [39] achieved up to a 70% detection rate for people detection in thermal imagery based on a cascaded classification technique combining additional multivariate Gaussian shape matching. [PERSON] and [PERSON] [1] used GeoEye-1 satellite images to detect people using an adaptive kernel density estimation method and estimated the corresponding probability density function; their results reached an approximately 75% detection success rate. [PERSON] et al. [4] used Mask R-CNN to detect people from cameras in  
Figure 11: Detection of people using Faster R-CNN: without data augmentation but with a buffer (**a**); with data augmentation and a buffer (**b**). Red—ground truth; green—without a mask; and blue—with a mask. Location 7 (pixel resolution is 30 \(\times\) 30 cm).  
Figure 10: Detection of people using Faster R-CNN: without data augmentation or a buffer (**a**); with data augmentation but without buffer (**b**). Red—pixels with people (ground truth); green—the most probable detected location without a mask; and blue—the most probable detected location with a mask. Location 7 (pixel resolution is 30 \(\times\) 30 cm).  
offices and shops and achieved an AP of 56-76%. Using a modified Faster R-CNN method, [PERSON], [PERSON], and [PERSON] [24] detected small objects, such as planes and ships; the AP for planes was approximately 83% and for ships it was 71%. [PERSON] et al. [40] proposed a method for extracting tailing pond information from high-resolution images (Google Earth images) using Faster R-CNN, and their AP reached 80.1%. Testing models with other architectures (YOLOv3, SSD, and RetinaNet) using ArcGIS Pro software implementation did not achieve higher accuracy than Faster R-CNN.