Earth Observation Imagery

Earth observation imagery analysis uses satellite and aerial images to monitor and understand Earth's surface, focusing on applications like land-use classification, flood mapping, and object detection. Current research heavily utilizes deep learning, employing architectures such as convolutional neural networks (CNNs), transformers, and generative models (e.g., diffusion models and GANs) for tasks ranging from semantic segmentation to anomaly detection. These advancements improve the accuracy and efficiency of image processing, enabling more precise monitoring of environmental changes and supporting improved decision-making in various fields, including agriculture, disaster management, and urban planning. The development of large-scale foundation models and readily available datasets is driving progress in this rapidly evolving field.