This paper presents BiCrossNet, a novel approach to cross-view geolocalization utilizing binary neural networks to significantly reduce computational complexity while maintaining competitive performance. Key contributions include the development of a Bi-Gradual Unfreezing method to enhance transfer learning, a Bi-Partitioned Optimization strategy to improve training stability, and the use of logit-based knowledge distillation to supplement standard losses. Experimental results on the University-1652 and SUES-200 datasets demonstrate that BiCrossNet establishes a new benchmark in the efficiency-performance trade-off. It achieves up to a 90.87-fold reduction in operations and uses 4.64 times less disk space compared to similar-performing state-of-the-art models on the SUES-200 dataset, and a 30-fold reduction in operations and 5.13 times less disk space on the University-1652 dataset. The code is available at https://anonymous.4open.science/r/BiCrossNet-FB7A/README.md.
