Compact Convolutional Segmentation for Visual Landmark Extraction in GNSS-Denied UAV Navigation
Reliable localization of unmanned aerial vehicles (UAVs) becomes challenging when Global Navigation Satellite System (GNSS) signals are degraded, blocked, or intentionally jammed. In such GNSS-denied conditions, visual information obtained from onboard cameras can provide complementary cues for navigation by identifying spatially stable and distinc tive landmarks. This study proposes a compact convolutional segmentation framework for extracting candidate visual land marks from aerial imagery. The proposed model combines fully convolutional processing with dilation-based spatial con text extraction and residual feature transfer. Since a dedicated UAV landmark dataset is not available in this study, an aerial building segmentation dataset is adapted as an initial evaluation environment. Experimental results indicate that the proposed architecture provides a feasible front-end for candidate landmark extraction, while further improvements are required through extended training, UAV-specific datasets, and integration with localization or matching algorithms.