Geospectra: leveraging quantum-SAR and deep learning for enhanced geolocation in urban environments

Geolocation services are crucial for applications such as navigation, mapping, and location-based services, but conventional methods often face challenges related to scalability, cost, and accessibility. This paper presents GeoSpectra, a groundbreaking framework aimed at democratizing access to precise geolocation while supporting sustainable urban planning. GeoSpectra utilizes the QuantumSAR Dataset, a comprehensive collection combining Sentinel-1 SAR imagery, proprietary synthetic data from quantum sensing, and additional datasets like SynthWakeSAR and SIVED. The framework employs convolutional neural networks and recurrent neural networks, optimized for supercomputing environments, to process this extensive dataset. The dataset, featuring 3,000 high-resolution synthetic SAR images and various real-world scenarios, enhances model training and validation. GeoSpectra’s models achieved impressive performance, with accuracy rates of 95% for QuantumSAR, 92% for SynthWakeSAR, and 97% for SIVED. Precision and recall metrics are similarly high, reflecting the framework’s robustness. By advancing geolocation accuracy, GeoSpectra aims to improve urban mobility, reduce traffic congestion, and minimize environmental impacts. This research underscores the transformative potential of deep learning in refining traditional geolocation technologies and fostering smarter, more sustainable urban environments.