This paper reviews state-of-the-art methods for UAV navigation in GNSS-denied environments, focusing on map-based spatial matching and vision-based techniques. It identifies trends and challenges in current approaches, emphasizing the integration of cartographic data, visual odometry, and data fusion. A novel methodology for identifying research gaps is presented, combining systematic literature review, qualitative and quantitative analysis with multi-criteria evaluation. The findings highlight limitations in existing methods and propose a navigation system that leverages spatial pattern matching and object recognition, integrating vision-based and GIS data. This approach addresses real-time processing challenges and offers a foundation for improving UAV navigation accuracy in GNSS-denied settings.