## 2 Related Work  
The state of the art in semantic pixel-wise segmentation research can be broadly classified into two different types, namely Traditional and Deep-Learning (DL) based methods. Since 2012, the development of CNN-based segmentation models has been initiated in the early stages by the Fully Convolution Neural Network (FCN) [11]. The FCN has the advantage of using the original size of the image with fewer parameters and lower computational time. The most commonly used DL techniques in satellite and aerial imagery analysis were Autoencoders and CNN. Inspired by the performance of existing autoencoders [12] and to overcome the limitations of the results of the coarse  
Figure 1: The general design of encoder-decoder-based Semantic Segmentation Models (SSM).  
segmentation method, encoder-decoder-based segmentation models such as SegNet [13] and U-shaped networks were introduced. After the successful development of CNNs such as VGG16, MobileNet, and ResNet as object classifiers, researchers were motivated to use them for prediction tasks such as segmentation. Therefore, a new method was introduced for combining the CNN model as an encoder and decoder of the base model [1].  
In [14], the SegNet-based segmentation model was developed using the VGG16 network as the encoder backbone for feature extraction. They analyzed the tradeoff between memory space and segmentation accuracy when implemented in the CamVid dataset to understand the road scene. As a result, SegNet has a reduced number of parameters and is efficient in both memory and processing time. In 2016, a new method of dilated convolution was proposed to enhance the resolution of the obtained feature map [15]. In this method, pixels are skipped without a pooling operation to preserve the image's resolution during processing and increase the accuracy when integrating with existing segmentation models.  
Another model for image segmentation is DeepLabV1, DeepLabV2, and DeepLabV3, which is state of the art in using CNN for feature extraction, dilated convolutional layers, and Control Random Fields (CRF). They employed the Atrous convolution layer for producing a larger feature map in the output without increasing the parameters and losing resolution, which is best for better segmentation performance. A novel network called ENet (Efficient Neural Network), similar to SegNet, was proposed in 2016 by [PERSON] for handling lower latency [16]. The dilated convolutional and stacked residual layers are used to maintain accuracy. In 2020, a new proposed architecture called Eff-UNet (Efficient UNet) [1] was introduced, which combines the effects of EfficientNet as an encoder backbone architecture with the U-net decoder. This model's performance was evaluated in an unstructured IDD-Lite dataset and outperformed other architectures by winning the first prize in the segmentation competition. Similarly, the U-Net model's accuracy has improved by replacing the U-net's original encoder with the pre-trained classification network VGG11 [17].  
Based on this survey of different segmentation models, we modeled our required network using the different encoders with the predefined decoder using the transfer learning technique.