High Resolution Aerial

High-resolution aerial imagery analysis focuses on extracting meaningful information from detailed aerial photographs, primarily for applications like automated map creation, ecological monitoring, and urban planning. Current research emphasizes the use of deep learning, particularly convolutional neural networks (CNNs) and transformers, often combined with generative adversarial networks (GANs) for image enhancement and super-resolution, to achieve accurate semantic segmentation and object detection. These advancements are improving the efficiency and accuracy of tasks such as land cover classification, tree crown delineation, and road network mapping, leading to significant improvements in various fields requiring precise geospatial data.