## 1 Introduction  
The availability of high-resolution satellite images and development of methodologies to extract roads from satellite images have revolutionized the remote sensing domain. Some sub-domains predominately impacted by this revolution include autonomous navigation, transportation management, urban development planning, and so on. However, the diverse variations in road structures introduce multi-scale characteristics, which in turn lead to limitations in accurate road extraction. Additionally, the sparsity of roads and the presence of shadows in remote sensing images present significant challenges in the automatic and systematic extraction of roads from satellite imagery. [PERSON] and [PERSON]'s [1] study presents pioneering work in automatic road detection from high-resolution satellite imagery on a large scale. They effectively incorporated a neural network with millions of trainable weights and successfully equipped the network to detect road objects in large datasets of urban imagery automatically. Following the work of [PERSON], several deep learning methods have been utilized to automate road detection and extraction from remote sensing images. The methods range from general deep learning algorithms [2; 3; 4] to task-specific models [5; 6; 7]. While these advanced deep learning methods have been successful in semantic segmentation tasks, such as extracting roads from satellite imagery, the downsampling operation in the convolution layers may lead to the loss of spatial information [8], a challenge also noted in feature selection for DDoS detection models [9] and in privacy-preserving federated learning approaches for medical image analysis [10]. To address this problem in semantic segmentation, atrous convolution was utilized in a study by [PERSON] et al. [11], which allows for learning semantic information without spatial loss. This work also proposed an Atrous Spatial Pyramid Pooling (ASPP) module that performed better in capturing multi-scale objects. This approach was expanded in DeepLabV3 [12], which can capture global context, followed by DeepLabV3+ [13], which incorporates a decoder module, with each progression showing improved performance in the semantic segmentation task. Another type of convolution called depthwise separable convolution, if used efficiently, can lead to better performance, as showcased by [PERSON] et al. [14], who used it in their proposed model called Xception, achieving state-of-the-art performance in a classification task. Due to its relevance and effectiveness in multi-scale feature capture, the DeepLab series has been explored for road extraction in the recent literature [15; 16; 17]. These studies mainly focus on altering the backbone feature extractor in the decoder, altering loss functions, or incorporating new backbone extractors, such as VGG19 [18], ResNet50 [19], and Xception [14], alongside the same ASPP module. Even though the ASPP module has successfully captured multi-scale patterns, it may not adequately capture complex multi-scale features in objects such as roads. The DenseASPP [20], proposed by [PERSON] et al., has effectively captured complex patterns in street scenes due to its capability to interpret dense features. However, despite its effectiveness, DenseASPP tends to be computationally intensive. Considering the contributions and limitations of these studies, our contributions to road extraction are as follows:  
* We propose an innovative module called Dense Depthwise Dilated Separable Spatial Pyramid Pooling (DenseDDSSPP) and replace the ASPP module with it in DeepLabV3+. * We conducted an experimental evaluation of various deep learning models to identify an optimal backbone network, which is Xception. * The present work integrates the Squeeze-and-Excitation block in the decoder to enable our road extraction process to focus on relevant feature channels from the dense feature map obtained.