## 2 Materials and Methods
### DLSS Data Construction
#### 2.2.3 U-Net, SegNet, DeepLabv3+ Algorithms  
Semantic segmentation is a deep learning algorithm that applies a segmentation label to the image in pixels [18]. The U-Net as a Semantic Segmentation algorithm is a type of supervised classification [28], which was first introduced in biomedical imaging to extract the cellular boundary data. As an artificial neural network applied in a variety of image segmentations, the U-Net algorithm allows the classification in pixels for the segmentation of objects in a given image so that it is characterized by the retention of the same resolution on the final classification output as the input, to be distinguished from other convolutional neural networks [22]. The U-Net algorithm is mainly used for image segmentation using a small amount of data [29; 30]. In this study, the learning data were constructed through \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**KD-2 Mapper**} & \multicolumn{2}{c}{**Mica Sense RedEdge-M**} \\ \hline
**Category** & **Specifications** & **Category** & **Specifications** \\ \hline Model & Keva Drone KD-2 Mapper & Model & Mica Sense RedEdge-M \\ \hline Wingspan & 1.8 m & Weight & 150 g \\ \hline Length & 1.1 m & Dimensions & 12.1 cm \(\times\) 6.6 cm \(\times\) 4.6 cm \\ \hline Weight & 2.6 kg & Power & 5.0 V DC, 4 W nominal \\ \hline Battery & Li-ion (11.1 V, 12 Ah) & Spectral Band & Blue, Green, Red, RedEdge, NearIR \\ \hline Cruise Speed & 40–50 km/h & Ground Sample Distance & 8.2 cm/Pixel (per band) at 120 m (400 ft.) AGL \\ \hline Operation Time & 60 min & & \\ \hline Operation Range & 5 km & Capture Speed & 1 capture per second (all bands), 12-bit RAW \\ \hline Wind Resistance & Cross wind 10 m/s & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specifications of KD-2 Mapper and Mica Sense RedEdge-M.  
vectorizing to extract the boundary data for PWDT from the ground truth based on time series UAV images. The SegNet algorithm is based on the concept of semantic segmentation, which recognizes objects on a pixel-by-pixel basis and predicts which object a pixel at that location represents. It is primarily motivated by road scene understanding applications which require the ability to model appearance (road, building) and shape (cars, pedestrians) and to understand the spatial relationship (context) between different classes such as road and sidewalk. The engine must also have the ability to delineate objects based on their shape despite their small size. Hence it is important to retain boundary information in the extracted image representation [22]. It uses convolution and maxpooling to speed up learning and has a network structure that minimizes the loss of location information through compression and upsampling [23]. Hence, it is designed to be efficient both in terms of memory and computational time during inference. It is also significantly smaller in the number of trainable parameters than other competing architectures. The key component of SegNet is the decoder network which consists of a hierarchy of decoders, one corresponding to each encoder. Of these, the appropriate decoders use the maxpooling indices received from the corresponding encoder to perform non-linear upsampling of their input feature maps. This idea was inspired from an architecture designed for unsupervised feature learning [31]. DeepLabV3+ extends DeepLabV3 by adding a simple yet effective decoder module to recover the object boundaries [32]. DeepLabV3 employs the spatial pyramid pooling module with the encoder-decoder structure. DeepLabV3+ contains rich semantic information from the encoder module, while the detailed object boundaries are recovered by the simple yet effective decoder module. The encoder module allows us to extract features at an arbitrary resolution by applying atrous convolution [33].