  Recent years have seen the rapid deployment of low-cost CubeSats in low-Earth
orbit, primarily for research, education, and Earth observation. The vast
majority of these CubeSats experience significant latency (several hours) from
the time an image is captured to the time it is available on the ground. This
is primarily due to the limited availability of dedicated satellite ground
stations that tend to be bulky to deploy and expensive to rent. This paper
explores using LoRa radios in the ISM band for low-latency downlink
communication from CubeSats, primarily due to the availability of extensive
ground LoRa infrastructure and minimal interference to terrestrial
communication. However, the limited bandwidth of LoRa precludes rich satellite
Earth images to be sent - instead, the CubeSats can at best send short messages
(a few hundred bytes).
  This paper details our experience in communicating with a LoRa-enabled
CubeSat launched by our team. We present Vista, a communication system that
makes software modifications to LoRa encoding onboard a CubeSat and decoding on
commercial LoRa ground stations to allow for satellite imagery to be
communicated, as well as wide-ranging machine learning inference on these
images. This is achieved through a LoRa-channel-aware image encoding that is
informed by the structure of satellite images, the tasks performed on it, as
well as the Doppler variation of satellite signals. A detailed evaluation of
Vista through trace-driven emulation with traces from the LoRa-CubeSat launch
(in 2021) shows 4.56 dB improvement in LoRa image PSNR and 1.38x improvement in
land-use classification over those images.
