Satellite Observation

Satellite observation leverages remote sensing data to monitor Earth's systems and processes, aiming for improved accuracy and efficiency in various applications. Current research emphasizes the use of machine learning, particularly deep learning architectures like convolutional neural networks (CNNs) and transformers, to analyze multi-modal and time-series satellite imagery, often incorporating techniques like transfer learning and self-supervised learning to address data scarcity and improve model generalization. This work is significant for advancing our understanding of climate change, environmental monitoring (e.g., deforestation, pollution, flooding), and improving the accuracy and timeliness of weather forecasting and disaster response.