This study addresses an image-matching problem in challenging cases, such as large scene variations or textureless scenes. To gain robustness to situations, most previous studies have attempted encode the global contexts of a via graph neural networks transformers. However, these do not explicitly represent high-level contextual information, structural shapes semantic instances; therefore, enco...

Historical imagery is characterized by high spatial resolution and stereo-scopic acquisitions, providing a valuable resource for recovering 3D land-cover information. Accurate geo-referencing of diachronic historical images means self-calibration remains bottleneck because the difficulty to find sufficient amount feature correspondences under evolving landscapes. In this research, we present fu...