Session
Weekend Session 4: Next on the Pad - Research & Academia
Location
Utah State University, Logan, UT
Abstract
As part of an initiative to promote the development and implementation of innovative technologies on-board Earth Observation (EO) missions, the European Space Agency (ESA) kicked off the first Φsat related activities in 2018 with the aim of enhancing the already ongoing FSSCAT project with Artificial Intelligence (AI).
The selected Φsat-2 concept will provide a combination of on-board processing capabilities (including AI) and a medium to high resolution multispectral instrument from Visible to Near Infra-Red (VIS/NIR) able to acquire 8 bands (7 + Panchromatic) provided by SIMERA SENSE Europe (BE). These resources will be made available to a series of dedicated applications that will run on-board the spacecraft. The mission prime is Open Cosmos (UK), supported by CGI (IT) to coordinate the payload operations for at least 12 months after LEOP and commissioning phase. During the nominal phase the various AI applications will be fine-tuned after the on-ground training and then routinely run.
A series of AI applications that could be potentially embarked are under development. The first one is called SAT2MAP and is expected to autonomously detect streets from acquired images. It is developed by CGI (IT).
The second AI application is an enhancement of the Φsat-1 cloud detection experiment, able to prioritize data to be downloaded to ground, based on standard cloud coverage and new concentration measurements. It is developed by KP Labs (PL) and it is based on a U-Ne. This application will mainly act as an on-board service for the other applications, relieving them of the task of assessing the presence of the clouds.
The Autonomous Vessel Awareness application aims to detect and classify various vessel types in the maritime domain. This would enable a reduced amount of data to be downloaded (only image patches including the vessel) improving the response time for final users (e.g maritime authorities). In this case the AI technique used is a combination of Single Image Super resolution (SRCNN) and Yolo-based Convoluted Neural Network (CNN).
The Deep Compression application generically reduces the amount of data to be downloaded to ground with limited information loss. The image is compressed on-board and then reconstructed on ground by means of a decoder. It can achieve a compression rate of about 7 per band. It is based on the use of a Convolutional Auto Encoder (CAE).
Two more AI applications will be selected by ESA through a dedicated challenge open to institutions, Agencies and industries that will be run in the first half of 2023. The Φsat-2 mission successfully passed the CDR phase at the end of 2022 aiming for a launch in 2024.
The ESA ΦSat-2 Mission: An A.I Enhanced Multispectral CubeSat for Earth Observation
Utah State University, Logan, UT
As part of an initiative to promote the development and implementation of innovative technologies on-board Earth Observation (EO) missions, the European Space Agency (ESA) kicked off the first Φsat related activities in 2018 with the aim of enhancing the already ongoing FSSCAT project with Artificial Intelligence (AI).
The selected Φsat-2 concept will provide a combination of on-board processing capabilities (including AI) and a medium to high resolution multispectral instrument from Visible to Near Infra-Red (VIS/NIR) able to acquire 8 bands (7 + Panchromatic) provided by SIMERA SENSE Europe (BE). These resources will be made available to a series of dedicated applications that will run on-board the spacecraft. The mission prime is Open Cosmos (UK), supported by CGI (IT) to coordinate the payload operations for at least 12 months after LEOP and commissioning phase. During the nominal phase the various AI applications will be fine-tuned after the on-ground training and then routinely run.
A series of AI applications that could be potentially embarked are under development. The first one is called SAT2MAP and is expected to autonomously detect streets from acquired images. It is developed by CGI (IT).
The second AI application is an enhancement of the Φsat-1 cloud detection experiment, able to prioritize data to be downloaded to ground, based on standard cloud coverage and new concentration measurements. It is developed by KP Labs (PL) and it is based on a U-Ne. This application will mainly act as an on-board service for the other applications, relieving them of the task of assessing the presence of the clouds.
The Autonomous Vessel Awareness application aims to detect and classify various vessel types in the maritime domain. This would enable a reduced amount of data to be downloaded (only image patches including the vessel) improving the response time for final users (e.g maritime authorities). In this case the AI technique used is a combination of Single Image Super resolution (SRCNN) and Yolo-based Convoluted Neural Network (CNN).
The Deep Compression application generically reduces the amount of data to be downloaded to ground with limited information loss. The image is compressed on-board and then reconstructed on ground by means of a decoder. It can achieve a compression rate of about 7 per band. It is based on the use of a Convolutional Auto Encoder (CAE).
Two more AI applications will be selected by ESA through a dedicated challenge open to institutions, Agencies and industries that will be run in the first half of 2023. The Φsat-2 mission successfully passed the CDR phase at the end of 2022 aiming for a launch in 2024.
