Nadir Aerial Image

Nadir aerial images, taken directly overhead, are ideal for many applications, but off-nadir images—taken at an angle—are often more readily available and require specialized processing. Current research focuses on developing robust algorithms, frequently employing deep learning architectures like U-Net and Mask R-CNN, to address challenges posed by off-nadir perspectives, such as building footprint extraction, crater detection, and change detection. These advancements are crucial for improving the accuracy and efficiency of various applications, including urban mapping, autonomous navigation (especially in space exploration), and disaster response.