Call for Papers
The emergence of new and more powerful sensor technologies on various platforms (esp. satellites and UAVs) has produced a surge in remote sensing data availability and variety. While a growing number of satellite constellations map the Earth’s surface with increasing detail and frequency, drones of various kinds are gathering data locally.
To make use of these massive amounts of data in an efficient and fast way, computational intelligence tools are increasingly being used for pre-processing, cleaning and enhancing data, and for specific tasks such as classification, segmentation, construction of thematic maps, change detection, super-resolution, object detection and subpixel analysis. As a result, the success of deep learning approaches has injected new vitality in various research fields and introduced the use of remote sensing data to new applications.
In this Special Issue, we emphasize innovative state-of-the-art computational intelligence techniques and algorithms, including deep learning architectures, transfer learning, model fusion and evolutionary learning as well as new and promising fields such as neuromorphic computing.
Topics covered in this Special Issue:
- Advanced AI architectures for remote sensing information extraction;
- Conversion of classical RS models using AI;
- Transfer learning and cross-sensor learning;
- Model and data fusion;
- Service robotics systems (UAV, AGV) for safe and remote measuring, inspection, and monitoring;
- Advanced AI-based image feature extraction
- Neuromorphic computing;
- Evolutionary learning and metaheuristics.
Credits and Sources
| AI_ComputationalRS 2022 : Artificial Intelligence in Computational Remote Sensing|