A Deep Convolutional Network to Extract Real-Time Landmarks for UAV Navigation

Recent advances in satellite and communication technologies have significantly improved geographical information and monitoring systems. Global System for Mobile Communications (GSM) and Global Navigation Satellite System (GNSS) technologies, which rely on electromagnetic signals transmitted from satellites and base stations, have long been utilized for geolocation applications. However, signal attenuation due to environmental conditions or intentional interference such as jamming may lead to severe degradation or complete loss of positioning capability. In such GNSS-denied environments, landmark extraction becomes critical for the navigation of unmanned aerial vehicles (UAVs) used in monitoring applications. By processing images captured from onboard UAV cameras, reliable visual landmarks can be identified to enable navigation without GNSS support. In this study, a convolution-based deep learning approach is proposed for the extraction of appropriate landmarks, and its effectiveness is examined.