Automatic Road Extraction and Alert Generation from Satellite Imagery Using Deep Convolutional Neural Networks

Abstract: The rapid urbanization and infrastructural development globally necessitate accurate, up-to-date cartographic data. Traditional road extraction methods relying on manual digitization of satellite imagery are time- consuming, expensive, and prone to human error. This research paper presents a fully automated system for road extraction and alert generation designed to detect newly constructed roads in high-resolution satellite imagery using deep Convolutional Neural Networks (CNNs). The proposed architecture leverages a modified U-Net model with a ResNet backbone to perform pixel-wise semantic segmentation of road networks. Extracted binary masks are subjected to morphological post-processing and vectorization to generate GeoJSON representations. A spatial intersection engine then cross-references these newly vectorized roads against existing baseline databases (such as OpenStreetMap or proprietary governmental GIS databases). Upon detecting unregistered or novel infrastructural additions, the system triggers automated webhook alerts and dashboard notifications to relevant authorities. Extensive evaluation on the Massachusetts Roads Dataset and custom temporal satellite data demonstrates that our model achieves a Dice Coefficient of 0.89 and an Intersection over Union (IoU) of 0.81, significantly outperforming legacy threshold-based GIS algorithms. Furthermore, the implemented event-driven architecture ensures low-latency alert distribution, enabling near real-time urban monitoring. This paper details the mathematical loss functions, topological vectorization constraints, and secure communication protocols that comprise the system.

Keywords: Deep Learning, Road Extraction, U-Net, Semantic Segmentation, Remote Sensing, GIS, Alert Systems, Webhooks.