Large Scale Remote Sensing Image

Large-scale remote sensing image analysis focuses on efficiently processing and extracting meaningful information from massive, high-resolution imagery. Current research emphasizes developing novel deep learning architectures, such as variations of UNet, Transformers, and Vision State Space Models (like Mamba), to overcome computational limitations and improve accuracy in tasks like semantic segmentation, instance segmentation, and super-resolution. These advancements are crucial for improving applications ranging from precision agriculture and urban planning to environmental monitoring and disaster response, enabling more effective analysis of large geographical areas. The field is also exploring weakly supervised and unsupervised learning techniques to reduce reliance on extensive manual labeling.