Changes

Sequoia is an open-source, 3U CubeSat that will demonstrate on-board image classification and processing with updateable machine learning models. The goal of the project is to obtain a high volume of scientifically important imagery for ecological and climatology research. Researchers have no need for images saturated with clouds or uninteresting areas—so why not filter them out with convolutional neural networks? We will retrain Sequoia’s deep learning with images taken by the satellite, uplinking improvements. The Stanford Student Space Initiative is developing deep learning models for forest fire risk assessment and detection and a number of other applications. The mission architecture is user-definable with the operator specifying desirable image locations or types and resolutions, and the satellite maximizing delivery of fully open-source images.

Sequoia is an open-source, 3U CubeSat that will demonstrate on-board image classification and processing with updateable machine learning models. The goal of the project is to obtain a high volume of scientifically important imagery for ecological and climatology research. Researchers have no need for images saturated with clouds or uninteresting areas—so why not filter them out with convolutional neural networks? We will retrain Sequoia’s deep learning with images taken by the satellite, uplinking improvements. The Stanford Student Space Initiative is developing deep learning models for forest fire risk assessment and detection and a number of other applications. The mission architecture is user-definable with the operator specifying desirable image locations or types and resolutions, and the satellite maximizing delivery of fully open-source images.

==Mission Goals==

==Mission Goals==

==Timeline==

==Timeline==

===Software===

===Software===

Official summary to be released soon...

Sequoia's software systems are logically split into two areas. There is a flight computer, the PyCubed, which controls all the low-level flight control functionality, including receiving commands from the ground, downlinking telemetry, running most GNC algorithms, controlling the power systems, and supervising our payload. The payload is a Raspberry Pi SoC that is equipped with three cameras and an S-Band radio. It collects images, processes them using ML models specific to a given research objective, and downlinks the results and images over its high-bandwidth radio.

===Guidence Navigation and Control===

===Guidence Navigation and Control===

Official summary to be released soon...

Official summary to be released soon...