The Φsat-2 mission from the European Space Agency (ESA) is part of Φsat mission lineup aimed to address innovative mission concepts making use of advanced onboard processing including Artificial Intelligence. Φsat-2 is based on a 6U CubeSat with a medium-high resolution VIS/NIR multispectral payload (eight bands plus NIR) combined with a hardware accelerated unit capable of running several AI applications throughout the mission lifetime. As images are acquired, and after the application of dTDI processing, the raw data is transferred through SpaceWire to a payload pre-processor where level L1B will be produced. At this stage radiometric and geometric processing are carried out in conjunction with georeferencing. Once the data is pre-processed, it is fed to the AI processor through the primary computer and made available to the onboard applications; orchestration is done via a dedicated version of the NanoSat MO Framework. The following applications are currently baselined and additional two will be selected via dedicated AI Challenge by Q3 2023: SAT2MAP for autonomous detection of streets during emergency scenarios; Cloud Detection application and service for data reduction; the Autonomous Vessel Awareness to detect and classify vessel types and the deep compression application (CAE) that has the goal of reducing the amount of acquired data to improve the mission effectiveness.
Analysis of satellite-acquired synthetic aperture radar (SAR) data provides a way to rapidly survey road conditions over large areas. This capability could be useful for identifying road segments that potentially require repair or at least onsite inspection of their condition due to changes in vehicular traffic associated with change in land use. We conducted a feasibility study focused on urban roads near the Southwest Research Institute (SwRI) campus in San Antonio, Texas. The roads near SwRI were affected by heavy truck traffic, they were easily inspected, and the age and construction of the pavement was known. TerraSAR-X (TSX) SpotLight (ST) satellite data were used to correlate radar backscattering response to pavement age and condition. Our preliminary results indicate that TSX radar imagery can be useful for detecting changes in pavement type, damage to pavement, such as cracking and scaling, and, occasionally, severe rutting. In addition, multitemporal interferometric analysis showed patches of settlement along two roads south of the SwRI campus. Further development of an automated approach to detect degradation of roads could allow transportation departments to prioritize inspection and repair efforts. The techniques also could be used to detect surreptitious heavy truck traffic in areas where direct inspection is not possible.
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