The recent advancements in remote sensing and traffic monitoring have seen a shift towards scalable and real-time solutions, leveraging satellite imagery and edge computing. Innovations in deep learning models, such as the integration of Dense Depthwise Dilated Separable Spatial Pyramid Pooling with DeepLabV3+, are enhancing the accuracy of road extraction from satellite images, addressing the challenges posed by multi-scale road structures. Additionally, the application of edge computing in Distributed Acoustic Sensing (DAS) is enabling real-time traffic monitoring with low latency, offering new possibilities for both traffic analysis and structural health monitoring. These developments collectively push the boundaries of what is possible in traffic surveillance and urban planning, with significant implications for global traffic dynamics and infrastructure management.