Hey! Welcome to the Stanford Satellite Team! Whether you're an incoming frosh, alumni, PhD, or even not a part of the Stanford community, we're glad you're here. Our current mission Project Sequoia is a 3U (about the size of a long shoe box) fully in-house imaging satellite that will demonstrate CubeSats as a user-defined service with changeable machine learning models. Current Satellite Team Leads:
Stanford Affiliated ☀️
Outside Stanford ❄️
If you're not affiliated with Stanford but are interested in the team and our project, please email the team leads Akasha Hayden and Ian Chang (email@example.com, firstname.lastname@example.org).
- Structures (Mechanical Engineering) — Learn to create the structure of our satellite via aluminum laser-cutting, 3D printing, and computer-aided design (CAD)
- Software (Computer Science) — Learn to program our onboard flight computer and science computer, write machine learning models, and design a full software architecture.
- Avionics (Electrical Engineering) — Learn electrical engineering in space, make your own camera, radio, solar panels, and design your own custom circuit boards (we'll even teach you how to make rave lighting for your room).
- Guidance, Navigation, and Control (Aerospace Engineering/CS/EE/ME) — Learn how to guide our satellite where practical physics and coding meet, control pointing of the system by interacting with the Earth's magnetic field, and unspin the satellite after a rocket shoots it out.
- Satellite Systems Engineering — Talk with top space representatives in both government and industry, attend local satellite and space conferences, and learn how to launch a satellite safely and legally 😉. Also, check out the Policy team.
Sequoia 2019-Present 🌲
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 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. 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.
Here's our in-progress github (not in any finalized state) for the project. A full public project rundown of all systems, code, and designs will be released before we launch.
The principal work of Stanford Satellite Team has been the development of various Optical Communications technologies. POINTR, a 1U segment of a 3U cubesat launched in 2018 but never conected with ground control due to improper oribit insertion from the launch provider.
In addition, members have worked in Stanford faculty labs to build:
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Pages in category "Satellites"
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