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
Self-Configuring nnU-Nets Detect Clouds in Satellite Images
Bartosz Grabowski, Maciej Ziaja, Michal Kawulok, Nicolas Longépé, Bertrand Le Saux, Jakub Nalepa
IQUAFLOW: A new framework to measure image quality
P. Gallés, K. Takats, M. Hernández-Cabronero, D. Berga, L. Pega, L. Riordan-Chen, C. Garcia, G. Becker, A. Garriga, A. Bukva, J. Serra-Sagristà, D. Vilaseca, J. Marín