## 6 Conclusions  
Road extraction is one of the most important research areas for applications such as autonomous navigation and smart city planning, yet it faces several challenges. Our study addresses these challenges by advancing the capabilities of the DeepLabV3+ model through the introduction of the Dense Depthwise Dilated Separable Spatial Pyramid Pooling (DenseDDSPP) module, replacing the standard ASPP module. The study also identifies Xception as the optimal backbone network for enhanced feature extraction and integrates the Squeeze-and-Excitation block into the decoder, facilitating channelwise learning to emphasize relevant features. To the best of our knowledge, our work is the first attempt to incorporate Dense Depthwise Dilated Separable Convolution to form a DenseDDSPP module within a semantic segmentation model for road extraction from satellite imagery. The proposed approach demonstrated better performance using publicly available datasets including the Massachusetts road dataset and the DeepGlobe road dataset. Our model outperformed several state-of-the-art models based on various evaluation metrics in a supervised learning setup, improving the IOU, Precision, and \(F_{1}\) Score metrics. The visual comparisons further highlighted our model's ability to accurately extract and connect road segments, even in scenarios where roads were occluded by trees.  
Future work will focus on enhancing the generalizability of our model by collecting additional datasets and evaluating its performance in zero-shot scenarios [36], enabling the semantic segmentation of road types not encountered during training. We also aim to transition from a supervised setup, which requires annotated images for training, to a self-supervised learning approach. Inspired by recent advancements in self-supervised learning, such as those by [PERSON] et al. [23] and [PERSON] et al. [37], we plan to develop methods for road extraction that do not rely on corresponding annotated images for each satellite image. This approach holds the potential to significantly broaden the applicability of our model to diverse and unannotated datasets. Furthermore, while the present work integrates the SE block as the attention mechanism, future work will explore other attention modules, such as CBAM [38] and ECA-Net [39], to perform a comparative analysis and identify a lightweight, optimal attention module for road extraction.  
Conceptualization, A.M.; methodology, A.M.; validation, N.R., L.D., and S.M.S.; formal analysis, A.M., M.R.K.K., and L.D.; investigation, A.M., M.R.K.K., and W.W.; writing--original draft, A.M.; writing--review and editing, N.R., S.M.S., L.D., M.R.K.K., and W.W.; visualization, A.M. and M.R.K.K.; supervision, N.R., L.D., and S.M.S.; funding acquisition, N.R. All authors have read and agreed to the published version of the manuscript.  
This material is based in part upon work supported by the National Science Foundation under Grant Nos. CNS-2018611 and CNS-1920182 and by the Florida Department of Environmental Protection, Grant C-2104.  
The datasets used in this article are publicly available datasets for research purposes; the Massachusetts road dataset can be obtained from [1] and the DeepGlobe road dataset can be obtained from [33]. A few examples of images with road extraction in this study are presented in Figures 6 and 7. The complete dataset with road extraction in this research is available upon request from the corresponding author.  
The authors express their gratitude to the reviewers and editors for their insightful comments and constructive suggestions, which improved the quality and clarity of this paper.  
The authors declare no conflicts of interest.