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Space Lab

Edge learning has the potential to revolutionize the development of AI for space applications and AI Sweden’s Space Lab is exploring technologies that can be used for high-impact use cases.

Swarm and aggregation

Why edge learning in space?

Today, AI models are trained by transferring as much data as possible from sensors to a central storage and compute location. In general, the more data, the better the quality of the resulting model. However, as the number of satellites increases, the available bandwidth to transfer data will become less and less. Thus, data transfer costs are expected to rise, it will take longer to transfer enough data to Earth to train models, and the models will be less accurate if less data is transferred. 

Edge learning (also known as decentralized learning, federated learning, or swarm learning) is a technology that has the potential to revolutionize the development of AI for space applications by solving these challenges. This would save both money and resources since there is no need to send large amounts of data to the Earth.

Projects

Zenseact Edge AnnotatationZ Dataset

Edge AnnotationZ Challenge

AI Sweden, Zenseact, RISE, Lund University, Chalmers Industriteknik, and Univrses, in collaboration with CGit and MobilityXLab, invited the AI community to work on one of the key challenges of...
A satellite approaching the viewer, captured from an aerial perspective, with a backdrop of blue sky and clouds below

Space Data Hackathon

One of the largest challenges when it comes to space data analysis is cloud occlusion, which means that areas on the images are fully or partially covered by clouds and hamper the analysis. By...
An image displaying satellites in space above earth

SpaceEdge

SpaceEdge is the world’s first open testbed for space app development with the possibility for developers to upload their apps to SpaceCloud in-orbit.
Satellite image taken above Sweden

Swedish Space Data Lab 1.0

The objective of the Swedish Space Data Lab is to increase the use of data from space for the development of society and industry and for the good of the planet. Swedish Space Data Lab, is a...
An image displaying predicted cloud thickness

Swedish Space Data Lab 2.0

The National Space Data Lab 2.0 (SDL2.0) is a collaborative project led by AI Sweden, RISE, Luleå Technological University (LTU), and the Swedish National Space Agency (Rymdstyrelsen), and funded by...

Research

PAseos Simulates the Environment for Operating multiple Spacecraft

PASEOS is an open-source Python module that is capable of modeling operational scenarios involving one or multiple spacecraft. It considers several physical phenomena including thermal, power, bandwidth, and communications constraints as well as the impact of radiation on spacecraft. PASEOS can be run both as a high-performance-oriented numerical simulation and/or in a real-time mode directly on edge hardware. Find the code here and the accompanying paper here.

 

PAseos

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For more information, contact

Chiara Ceccobello
Chiara Ceccobello
Data Scientist