# A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification
## 2 Materials and Methods
### Methods  
#### 2.2.1 A Brief Review of Used Deep Learning Models for Transfer Learning  
The DenseNet, introduced by [PERSON] et al. [40], is a convolutional neural network that connects each layer to every other layer in a feed-forward manner. Unlike traditional convolutional networks, which have one connection between each layer and its next layer, the DenseNet incorporates all previous layers' representations as inputs for each layer. Moreover, each layer's own feature maps are used as inputs for all subsequent layers, resulting in a network with \(L(L+1)/2\) direct links, where \(L\) is the number of layers in the network [40]. Research conducted by [PERSON] et al. [41] analyzed model scaling and exhibited that performance may be improved by scrupulously offsetting network depth, thickness, and resolution. They proposed a novel scaling technique, Efficientnet, that employs a straightforward though incredibly powerful coefficient to equally scale all depth, extent, and resolution parameters [41]. [PERSON] et al. also presented the EfficientNetV2 family of convolutional networks [42], which exhibit faster training speeds and higher parameter efficiency than the original EfficientNet [41]. They demonstrated that EfficientNetV2 models can be trained in a shorter time than state-of-the-art models while also being up to 6.8 times smaller [42]. The authors noted that gradually increasing the image size during training can further speed up the process, but this may lead to a decrease in accuracy. To address this accuracy drop, they proposed a progressive learning strategy that dynamically adjusts regularization, such as data augmentation, along with image size [42]. [PERSON] et al. [43] introduced the MobileNets family of efficient models for mobile and embedded computer vision applications. MobileNets use a simplified architecture that employs depth-wise separable convolutions to build compact deep neural networks [43]. The authors proposed two simple global hyper-parameters that effectively balance training time and accuracy [43], enabling model developers to choose an appropriately sized model for their specific application based on the problem's constraints. [PERSON] et al. [44] improved the performance of mobile models on various tasks and benchmarks by introducing MobileNetV2, which is designed to perform well across a range of model sizes. [PERSON] et al. [45] utilized the quantized linear function to explain the widths and depths of successful networks and developed RegNet based on these parameters. Through an analysis of the RegNet design space, they uncovered some interesting resultsthat challenge the conventional approach to network design. The authors claim that RegNet models may outperform well-known EfficientNet models while being up to five times faster on GPUs with similar training settings [45]. AlexNet, proposed by [PERSON] et al. [46], is a CNN architecture that was developed for object detection. Although obtaining access to large-scale databases is a significant obstacle in CNN training [47], AlexNet was trained on the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) database [48], along with augmentation techniques. On the other hand, [PERSON] et al. [28] presented the VGG16 architecture for the object recognition task. VGG19, an enhanced version of VGG16, improves upon the limitations of AlexNet and enhances the system's accuracy. [PERSON] et al. introduced ResNet [29], a framework for residual learning that makes it easier to train deeper networks than those previously used. Instead of learning non-referenced functions, they rearranged the layers to learn residual functions based on the layer inputs. They presented extensive experimental evidence showing that these residual networks are simpler to tune and can benefit from much greater depth [29]. They evaluated residual networks up to 152 layers deep on the ImageNet dataset [49], which is eight times deeper than VGG networks while still being less complex [29]. The ResNet architecture was reviewed by [PERSON] et al.