Road Network Extraction Using CNN Architectures

The rapid advancement of satellite imagery and deep learning technologies has opened new avenues for automated geospatial data extraction and mapping. This research presents a novel approach to road network extraction from high-resolution satellite imagery, leveraging deep learning techniques and the Spacenet dataset to achieve over 99% of accuracy and DICE coefficient without overfitting by accurately identifying and delineating road networks from satellite imagery, and moreover, presenting an approach to optimize the post-processing with the ultimate goal of contributing to open-source mapping platforms like OpenStreetMap (OSM). Also, the manuscript highlights challenges and opens avenues for future research, including developing new metrics akin to the DICE coefficient and optimizing computational efficiency for model training with limited resources. By implementing and comparing three distinct Convolutional Neural Network (CNN) architectures using deep learning capabilities, the research systematically evaluated the performance of road network extraction techniques. The proposed methodology encompassed comprehensive data pre-processing, advanced deep learning model training, and post-processing strategies to transform raster road network predictions into vector data suitable for geospatial analysis. The research workflow demonstrated a seamless integration of satellite imagery analysis, deep learning road extraction, and open-source mapping platforms, thereby advancing automated geographic information system (GIS) methodologies with vectorized outputs suitable for seamless integration into mapping software platforms. The manuscript highlights the potential of integrating deep learning techniques with professional GIS software to enhance road network mapping and contributes to the expanding field of automated geospatial intelligence.