## 2 Materials and Methods  
### Data Collection and Preparation  
The data for this paper was collected using an operating TSF in Poland using a DJI Matrix 300 RTK unmanned aerial vehicle. DJI Zenmuse P1 fullframe 45 MP digital camera combined with a 35 mm lens was used for data acquisition (see Figure 1). The process of converting drone images into a metrically accurate orthophoto map involves several key steps. First, aerotriangulation is performed, which involves detecting thousands of common points between the images and calculating the geometric relationships between them. This is followed by generating a dense point cloud, which forms the basis for creating a digital terrain model (DTM). The orthomosaic is then produced by projecting the original images onto the terrain model, ensuring that the final map is geometrically correct. Common software used for this workflow includes Agisoft Metashape 2.1.4. and Bentley ContextCapture 2023. Figure 1: DJI Matrix 300 RTK unmanned aerial vehicle. During a single drone flight, data were collected to automatically delineate the beach line. The DJI P1 camera captured images covering a terrain width of 170 m. The flight lasted 20 min at an altitude of 120 m, with each image having a ground sample distance (GSD) of 1.5 cm. A total of 528 images were taken, amounting to 4.5 GB of data. The resulting orthophoto map had a pixel resolution of 10 cm. Weather conditions were challenging, with prolonged rainfall prior to the flight and heavy cloud cover at low altitude, reducing visibility. Examples of the final images composed of the raw ones are presented in Figure 2. For the purposes of this article, seven such fragments were used. The process of data preparation and beach line detection included several steps, presented in Figure 3. First, the line was detected manually by an expert. Then, the image was cropped into square fragments with a 100 \(\times\) 100 resolution. The fragments were manually separated into two classes: the beach and the water. Figure 4 shows example image fragments with assigned labels. The final learning sample included 2769 examples of the beach and 2772 fragments with the water. The data were split into training and validation samples in a 70/30 ratio. Based on these data, a binary image classifier was trained, which allows us to assign either a \"beach\" or a \"water\" label to each fragment of the image, based on which the beach line can be defined as. Figure 2: Examples of images collected for automatic beach line recognition. Convolutional Neural Network (CNN) architecture was selected to perform this task. CNNs are a class of deep learning models primarily designed for processing grid-like data, such as images. A typical CNN consists of multiple layers, including convolutional layers, pooling layers, and fully connected layers, which work together to extract and classify features from input data. The core of a CNN is the convolutional layer, where small filters or kernels slide over the input image, performing element-wise multiplication to detect local patterns, such as edges or textures. This is followed by pooling layers, often max pooling, which reduce the spatial dimensions of the data while retaining important information, thus improving computational efficiency and reducing the risk of overfitting. Finally, fully connected layers are used for classification tasks, where the learned features are mapped to output labels. CNNs have been highly successful in various tasks, including image classification, object detection, and segmentation, due to their ability to automatically learn hierarchical features from raw data [49; 50]. For the purpose of this article, a MobileNetV2 pre-trained convolutional model was selected. Compared to heavier models like ResNet or Inception, MobileNetV2 offers a  
Figure 4: Examples of image fragments classified as water and beach, divided into training, validation, and test samples.