Satellite Image
Satellite image analysis is a rapidly evolving field focused on extracting meaningful information from Earth observation data for various applications. Current research emphasizes the use of deep learning, particularly convolutional neural networks (CNNs) and vision transformers (ViTs), for tasks such as object detection, segmentation, and classification, often incorporating techniques like attention mechanisms and transfer learning to improve efficiency and accuracy. These advancements are significantly impacting fields like environmental monitoring, urban planning, disaster response, and precision agriculture by enabling automated and large-scale analysis of geospatial data.
Papers
DiffusionSat: A Generative Foundation Model for Satellite Imagery
Samar Khanna, Patrick Liu, Linqi Zhou, Chenlin Meng, Robin Rombach, Marshall Burke, David Lobell, Stefano Ermon
Active Wildfires Detection and Dynamic Escape Routes Planning for Humans through Information Fusion between Drones and Satellites
Chang Liu, Tamas Sziranyi
Natural Disaster Analysis using Satellite Imagery and Social-Media Data for Emergency Response Situations
Sukeerthi Mandyam, Shanmuga Priya MG, Shalini Suresh, Kavitha Srinivasan
FedFusion: Manifold Driven Federated Learning for Multi-satellite and Multi-modality Fusion
DaiXun Li, Weiying Xie, Yunsong Li, Leyuan Fang
Transformer-based nowcasting of radar composites from satellite images for severe weather
Çağlar Küçük, Apostolos Giannakos, Stefan Schneider, Alexander Jann
EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution
Yi Xiao, Qiangqiang Yuan, Kui Jiang, Jiang He, Xianyu Jin, Liangpei Zhang