Airport Detection

Airport detection, crucial for both civilian and military applications, focuses on automatically identifying airports in various image types, particularly satellite and synthetic aperture radar (SAR) imagery. Current research emphasizes developing robust and efficient deep learning models, such as adaptations of YOLO and other convolutional neural networks, often incorporating techniques like Shearlet transforms for improved feature extraction and handling of varying airport scales and orientations. The availability of large, annotated datasets is driving progress, enabling the development of more accurate and reliable algorithms with implications for autonomous navigation, disaster response, and improved situational awareness.