Safe-Smart Navigation System Using MachineLearning and Satellite Imaging

Women's safety during travel is a significant concern in urban and semi-urban areas, where traditional navigation systems like Google Maps prioritize efficiency over safety, neglecting risks such as poorly lit roads, crime-prone zones, and isolation. To address this issue, the propose system SafeRoute, a safety-first navigation system that integrates machine learning and satellite imagery to evaluate route safety in real time. Using CNN s for segmentation in satellite images, SafeRoute identifies critical safety features, such as infrastructure quality, crowd density, and proximity to buildings. Using these insights in combination with crime statistics, the system assigns safety scores to routes and recommends how best to travel securely. With Mapbox APIs ensuring accurate mapping and seamless navigation [1], SafeRoute achieved 85% accuracy in infrastructure detection and 90% user satisfaction in initial evaluations, demonstrating its potential to redefine navigation systems with a focus on safety. This research highlights the importance of adopting security-centric tools to ensure safer travel, especially for women.