🛰️ Welcome to Sats 🛰️
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.
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 Flynn Dreilinger and Grant Regen (firstname.lastname@example.org, email@example.com).
Sub-Teams and Schedules
- Structures — Learn to create the structure of our satellite via aluminum laser-cutting, 3D printing, and computer-aided design (CAD)
- Fall Meeting Schedule: Thursday 8PM PT
- Software — Learn to program our onboard flight computer and science computer, write machine learning models, and design a full software architecture.
- Fall Meeting Schedule: Thursday 6:30PM PT
- 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).
- Fall Meeting Schedule: Thursday 6PM PT
- Guidance, Navigation, and Control — 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.
- Fall Meeting Schedule: Wednesday 6PM PT
- Systems/Policy — 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 SSI Policy Team on the slack.
- Fall Meeting Schedule: Tuesday 6PM PT
|General Meeting||Sunday 10AM|
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.
What we've worked on in the past: POINTR, SNAPS
We are currently working on:
Sequoia, a 3U software defined imaging satellite that will demonstrate image classification with uploadable machine learning models
The principal work of Stanford Satellite Team has been the development of various Optical Communications technologies. In addition, members have worked in faculty labs to build:
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Pages in category "Satellites"
The following 21 pages are in this category, out of 21 total.