# People Detection Using Artificial Intelligence with Panchromatic Satellite Images
## 2 Materials and Methods  
### Background  
Among the various potential ML methods one could use, rapid progress in selected convolutional neural network (CNN) methods has been attracting the most attention recently. CNN methods are able to efficiently learn distinctive high-level features of object detection in remote sensing [7; 13]. CNN algorithms can be divided into two categories: so-called two-stage detectors (RCNN) and single-stage detectors (SSD, YOLO, and RetinaNet) [14; 15]. Faster R-CNN is used for generic object detection and has been successfully adapted from its two predecessors, R-CNN and Fast R-CNN [16; 17], to solve many recognition problems. Faster R-CNN consists of two modules: a region proposal network (RPN) and a Fast R-CNN detector [14; 16; 17]. Faster R-CNN improves object detection architecture by replacing the selection search algorithm in Fast R-CNN with a region proposal network (RPN) [14; 18]. An RPN is a fully convolutional network for proposal generation [14]. The rest of the model architecture remains the same as in Fast R-CNN. The system overview of Faster R-CNN is given in Figure 1.  
Figure 1: System overview of Faster R-CNN [19]. [PERSON] and [PERSON] [20] applied a CNN model to automatically detect and count African elephants in a woodland savanna ecosystem in South Africa. They used WorldView-3 and -4 satellite data. [PERSON], [PERSON], and [PERSON] [21] focused on creating a fast and reliable object detection algorithm that is trained on scenes depicting people in an indoor environment. This method combines YOLOv4 and Faster R-CNN. Additionally, [PERSON] and [PERSON] as well as [PERSON] and [PERSON] [22; 23] used Faster R-CNN to detect people in a sequence of video images. On the other hand, [PERSON] et al. [4] used Mask R-CNN, a representative extension of Faster R-CNN, which produces segmentation masks of objects. [PERSON], [PERSON], and [PERSON] [24] used a modified Faster R-CNN to detect ships and planes in optical remote sensing images (NWPU VHR-10). When compared to other popular object detection methods, like YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), or RetinaNet, Faster R-CNN offers distinct advantages that make it particularly effective for this specific application; Faster R-CNN's RPN generates high-quality region proposals that are more accurate and reliable compared to the single-stage approaches used in YOLO, SSD, and RetinaNet. The RPN is designed to propose regions that are likely to contain objects of interest, which is crucial for detecting small target objects like people in urban satellite imagery. This two-stage approach, where the RPN first proposes regions and then a classifier fine-tunes the detection, ensures higher precision and recall, which are critical for applications where accurate human detection is paramount [14; 16; 17; 18; 19; 25]. Although YOLO and SSD are known for their speed, they often sacrifice accuracy, particularly in complex and cluttered scenes. YOLO's single-stage detector processes the entire image concurrently, providing a faster detection time, but at the cost of reduced accuracy for small objects and increased false positives. SSD faces similar issues. RetinaNet introduces focal loss to handle the class imbalance problem (i.e., many more background examples than objects) in object detection [15; 26]. In contrast, Faster R-CNN's two-stage process allows for the more precise localization and classification of people, which is essential when dealing with the varied and detailed backgrounds of urban environments [25; 27]. Deep ML methods require a high volume of data, namely for their training [28; 29]. The number of training samples for object detection using CNN algorithms can vary widely depending on several factors, including the complexity of the task, the diversity of the objects, the sizes of the objects in the images, and the architecture of the CNN being used. CNN algorithms enable the extraction of image features, which can be effectively processed to reduce the dimensionality of the task [30].