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[[File:Sapling1-dish.jpeg|thumb|''Sapling Sempervirens'' (Sapling-1) at The Dish|alt=|400x400px]]

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{{Nowrap|Welcome to the SSI Satellites Team! Whether you're an incoming frosh, tired old senior, graduate student, alumni, or even not a part of the Stanford community, we're glad you're here. Our current mission is SAMWISE, a CubeSat with a huge number of technological advancements compared to our prior missions. Our Satellite Team Leads are }} {{Leadership|Satellites = true}}

{{Nowrap|Welcome to the SSI Satellites Team! Whether you're an incoming frosh, tired old senior, graduate student, alumni, or even not a part of the Stanford community, we're glad you're here. Our current mission is SAMWISE, a CubeSat with a number of technological advancements compared to our prior missions. Our Satellite Team Leads are }} {{Leadership|Satellites = true}}

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== Getting Started with Satellites ==

== Getting Started with Satellites ==

Slack channel: [https://ssi-teams.slack.com/messages/satellites-payload satellites-payload]            Subteam Lead: Niklas Vainio

Slack channel: [https://ssi-teams.slack.com/messages/satellites-payload satellites-payload]            Subteam Lead: Niklas Vainio

Welcome to the payload subteam! The payload often defines the mission of the satellite, so it's basically the most important part :) These payloads can be anything from telescopes like Hubble to communications like Starlink. Our recent satellites have largely focused on low-cost camera systems and radio modules. This subteam covers a huge variety of topics, so no matter your interests definitely join the Slack and reach out!  

Welcome to the payload subteam! The payload often defines the mission of the satellite, so it's basically the most important part :) These payloads can be anything from telescopes like Hubble to communication systems like Starlink. Our recent satellites have largely focused on low-cost camera systems and radio modules. This subteam covers a huge variety of topics, so no matter your interests definitely join the Slack and reach out!  

Our current projects are developing a multi-camera system and a higher speed radio module for the SAMWISE mission. This system is based on the Raspberry Pi architecture and will include an Earth-facing camera, a star tracker used in conjunction with our ADCS system, and a selfie camera! The higher speed radio will allow us to more quickly send images back down to Earth.

Our current projects are developing a multi-camera system and a higher speed radio module for the SAMWISE mission. This system is based on the Raspberry Pi architecture and will include an Earth-facing camera, a star tracker used in conjunction with our ADCS system, and a selfie camera! The higher speed radio will allow us to more quickly send images back down to Earth.

=== Sequoia: 2019–2020 ===

=== Sequoia: 2019–2020 ===

Sequoia was a planned 3U CubeSat that would demonstrate on-board image classification and processing with updateable machine learning models. The goal of the project was to obtain a high volume of scientifically important imagery for ecological and climatology research. Researchers many times have no need of 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. SSI worked on 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 was a planned 3U CubeSat that would demonstrate on-board image classification and processing with updateable machine learning models. The goal of the project was to obtain a high volume of scientifically important imagery for ecological and climatology research. Researchers many times have no need of images saturated with clouds or uninteresting areas—so why not filter them out with a convolutional neural network? We will retrain Sequoia’s deep learning with images taken by the satellite, uplinking improvements. SSI worked on 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.  

Project materials can be found in the [https://github.com/stanford-ssi/Sequoia Sequoia GitHub].

Project materials can be found in the [https://github.com/stanford-ssi/Sequoia Sequoia GitHub].