High Resolution Satellite

High-resolution satellite imagery is revolutionizing Earth observation, enabling detailed analysis across diverse applications from disaster response to agricultural monitoring. Current research emphasizes developing and adapting deep learning models, particularly convolutional neural networks (CNNs) and transformer-based architectures like U-Net and its variants, for tasks such as object detection, segmentation, and super-resolution. These advancements are significantly impacting various fields, improving the accuracy and efficiency of tasks ranging from building damage assessment and flood mapping to precision agriculture and environmental monitoring. The development of large, publicly available datasets is also a key focus, facilitating model training and benchmarking across different satellite sensors and geographic locations.