Conveners
Artificial Intelligence/Machine Learning
- Florent Manni (CNES (DSO/TB/ET))
- Alberto Urbón Aguado (Telespazio for ESA)
Artificial Intelligence/Machine Learning
- David Merodio Codinachs (ESA)
- Filomena Decuzzi
Artificial intelligence (Deep Learning) is everywhere. The space industry is no exception. Automated recognition of lunar craters for moon landings and identification of space junk using imaging could play important roles in securing space safety and advancing space exploration. Deep Learning is the most successful solution for image-based object classification, and for most practical...
GMV proposes a model-based approach for deep learning (DL) acceleration on FPGAs, taking on-board space debris detection as the target application. GMV has developed G-Theia1 smart-sensor integrating camera and high-performance processing logic into an embedded payload system. G-Theia1 is first proposed as a cost-effective space-based surveillance system in the project H038.3 SBSS-GNSS....
An increasing number of on-board processing applications require intelligent in-orbit processing to extract value-added insights rather than clog precious RF downlinks with bandwidths of data for post-processing on the ground. Some applications require autonomous, real-time decision making, e.g. a space-debris retrieval spacecraft outside of its ground-station coverage would not be able to...
In recent years, research in the space community has shown a growing interest in Artificial Intelligence (AI), mostly driven by systems miniaturization and commercial competition. Among the available devices for accelerating AI onboard satellites, Field Programmable Gate Arrays (FPGAs) constitute a valuable solution for their energy efficiency and low non-recurrent costs. To facilitate and...
