From Satellite to Shapefiles: <scp>ZymbaNet</scp> For Memory‐Efficient Automated Road Mapping

ABSTRACT Road network extraction from very‐high‐resolution (VHR) satellite imagery is a fundamental task in geospatial applications such as urban planning, navigation, and geographic information systems (GIS). Despite significant progress achieved by convolutional and attention‐based architectures, existing methods still face two critical challenges: high computational complexity and the absence of complete automated pipelines that generate GIS‐ready vector products. In this paper, we introduce ZymbaNet, a memory‐efficient deep neural architecture that integrates an enhanced State Space Model (E‐SSM) with Res2NeXt multiscale feature modules and PseudoMamba blocks for efficient long‐range dependency modeling. ZymbaNet reduces memory requirements by more than 50% compared to existing attention‐based networks while maintaining competitive segmentation accuracy. Beyond segmentation, we propose a fully automated road‐to‐GIS pipeline, which first georeferences the predicted road masks and then converts them into topologically valid shapefiles through skeletonization and Douglas‐Peucker geometric simplification. Extensive experiments on the Massachusetts Roads and DeepGlobe datasets demonstrate the superiority of our approach, achieving an F1‐score of 76.23% and 78.80% respectively, while significantly reducing the number of parameters and floating‐point operations (FLOPs). This framework bridges the gap between pixel‐level predictions and operational GIS‐ready cartographic products, providing an efficient and scalable solution for automated road mapping at large scale.