# People Detection Using Artificial Intelligence with Panchromatic Satellite Images
## 2 Materials and Methods
### Methodology
This transformation allows the model to become more robust to slight variations in the orientation of objects. * Zoom--Images were randomly zoomed up to 50%. Zooming helps create a variety of scales within the dataset. * Warp--Random generation of different deformation of images (including skewing, stretching, and shifting), within the specified 0.3 range. Warping simulates natural distortions and irregularities that might be present in real images. The use of the \"get transforms\" function is essential for data augmentation. This function applies a sequence of transformation operations to images during both the training and validation processes. These transformations are random and are applied every time the images pass through the model, meaning that each image may appear differently in each batch. This variability is key for increased data variability, which leads to improved model generalization and a reduced overfitting risk. As a result, the \"get_transforms\" function enhances the effectiveness of the training process, thereby improving its robustness and performance when encountering new, unseen data. Variants 15 and 16 represent outputs of training with data augmentation. Variant 15 uses the training data without a buffer, and the AP reached 42% (Table 3). When training data are applied with a buffer of 15 cm, the AP value increased to 49%. Based on the results  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Input Parameters**} & \multicolumn{1}{c}{**Optimization**} & \multicolumn{1}{c}{ \begin{tabular}{c} **Quality** \\ **Assessment** \\ \end{tabular}  } \\ \hline
**Variant** & **Buffer (cm)** & **Backbone** & **Batch Size** & **Epochs** & **Learning Rate** & \begin{tabular}{c} **Average Precision** \\ **Score (\%)** \\ \end{tabular}  \\ \hline
1. & 0 & Resnet101 & 8 & 50 & 0.000479 & 18.82 \\
2. & 0 & Resnet101 & 2 & 50 & 0.000575 & 14.15 \\
3. & 0 & Resnet101 & 32 & 50 & 0.000132 & 15.09 \\
4. & 0 & Resnet101 & 32 & 1  
obtained using the Resnet152 backbone, data augmentation was applied to other backbones as well. Subsequently, various additional backbone variants were tested, as shown in Table 3. None reached better results than ResNet152 with a 15 cm buffer. The variant with MobileNet V3 reached almost 43%, VGG16 and VGG19 37%, Inception V3 31.55%, and V4 only 0.29%. The DenseNet backbones (121, 161, 169, and 201) do not provide useful results (mAP about 1%) The best results among the tested backbones were achieved by the MobileNet V3 backbone, with an AP of 42.65%. Slightly lower results were recorded for the VGG16 and VGG19 backbones, where the AP was around 37%. The worst results were obtained using DenseNet, with an AP of approximately 1%. The training process and validation are depicted by the plotting of validation and training losses after fitting the model (Figure 3). In both cases, the function on the training data is decreasing, i.e., the model is learning, and its predictions are improving. When the loss function on the validation data decreases, it means that the model not only predicts well on the training data, but also generalizes to new data. This is important because it is necessary to have  
The trained model then needed to be tested on a sample of data that was not used during training. ArcGIS Pro with its \"Detect Objects Using Deep Learning\" function was employed to test the model. Four models were tested: variants 9 and 13 without data augmentation, and variants 15 and 16 with data augmentation. All four models were tested on an image without a mask and one with a mask (Figure 5). The predictions that had a confidence score higher than 50% were included in the result as predicted people. Simultaneously, the YOLOv3 model was trained with different settings (Table 4). According to the results of the Faster R-CNN testing, this testing was focused on models with data augmentation and a buffer of 15 cm. The batch size was set to 32, 300 epochs were used, and Darknet53 was used as a backbone.