FOOL: Addressing the Downlink Bottleneck in Satellite Computing with
  Neural Feature Compression
Alireza Furutanpey Qiyang Zhang Philipp Raith Tobias Pfandzelter Shangguang Wang Schahram Dustdar
Nanosatellite constellations equipped with sensors capturing large geographic
regions provide unprecedented opportunities for Earth observation. As
constellation sizes increase, network contention poses a downlink bottleneck.
Orbital Edge Computing (OEC) leverages limited onboard compute resources to
reduce transfer costs by processing the raw captures at the source. However,
current solutions have limited practicability due to reliance on crude
filtering methods or over-prioritizing particular downstream tasks.
  This work presents FOOL, an OEC-native and task-agnostic feature compression
method that preserves prediction performance. FOOL partitions high-resolution
satellite imagery to maximize throughput. Further, it embeds context and
leverages inter-tile dependencies to lower transfer costs with negligible
overhead. While FOOL is a feature compressor, it can recover images with
competitive scores on perceptual quality measures at lower bitrates. We
extensively evaluate transfer cost reduction by including the peculiarity of
intermittently available network connections in low earth orbit. Lastly, we
test the feasibility of our system for standardized nanosatellite form factors.
We demonstrate that FOOL permits downlinking over 100x the data volume without
relying on prior information on the downstream tasks.