ESA Phi-Lab Swedens's mission
ESA Phi-Lab Swedens's mission is to strengthen Sweden's and Europe's competitiveness in the global space sector by promoting the development of AI and edge learning technologies. In the coming years, ESA Phi-Lab Sweden will demonstrate how these technologies can be applied in the space sector and other industries.
The European Union's space policy is currently facing several challenges and opportunities, paving the way for initiatives like ESA Phi-LabNET. One of the most important needs that has been identified is that Europe needs to become more independent in terms of access to space. The global space economy is growing rapidly, driven by competitive private actors and an increasing number of space launches and satellites in operation.
Space is becoming increasingly scarce and contested. Today's geopolitical tensions clearly demonstrate the need for strategic autonomy, not only in terms of access to space, but also in several other technological areas linked to space activities, such as infrastructure, data collection and processing, and the development of space products and services. Although European countries have world-class space infrastructure, investment is at a lower level than that of global competitors and there is a lack of a unified legal framework within the EU.
AI is playing an increasingly important role in the space domain, for example in manufacturing, in-orbit operations, data collection and data analysis. It is therefore crucial to support efforts that help mature these technologies and pave the way for a successful establishment in the space market.
Against this background, and as part of ESA Phi-LabNET-initiativet, ESA Phi-Lab Sweden, run by RISE and co-funded by ESA and Vinnova, offers funding opportunities for project using AI and edge learning technologies in space applications. The initiative also aims to adapt space-based technologies for use in other sectors, while taking advantage of the EU's strategic investments and regulatory developments to promote innovation and strengthen competitiveness.
Thematic focus areas - AI, Edge, Hardware and Sustainability
AI and Edge Learning are revolutionizing Earth observation, remote sensing, and the broader space sector by enabling real-time data processing, improving image analysis, and integrating data from multiple sources. These technologies significantly improve object detection and tracking, climate monitoring, and data analytics, and enable AI models to process data directly on satellites, reducing the need for extensive data transfer.
Hardware development is critical for AI at the edge. Selecting and building intelligent edge hardware that can run AI models on local hardware, such as edge GPUs, TPUs, and specialized silicon platforms, enables fast and efficient computing directly on devices, improving the performance and reliability of AI applications in space.
To ensure sustainability, it is important to focus on energy-efficient co-development of hardware and software. This means designing AI algorithms and systems that use as little energy as possible and limit carbon emissions. Strategies such as energy-conscious task offloading, the use of renewable energy, and efficient resource use can significantly reduce Edge AI-system's environmental impact.
Innovations based on AI and Edge Learning have the potential to make the space sector more efficient and sustainable.
Project proposals should be groundbreaking, have great potential to impact society and the economy, and be based on solid research-driven knowledge. They should describe market demand and/or how research is driving future establishment of the proposed solution.
The idea should have a clear connection to the space area, for example through:
- Exploitation of space technology, application of knowledge from the space sector or use of space systems and space-based services in areas other than space (so-called spin-offs);
- Provision of products or services to the space sector, across the entire value chain (from individual components to completely new space systems, including facilities and services), as well as activities linked to the entire development chain (from concept phase to operation), possibly using technology originally developed outside the space sector, so-called spin-in.
Project proposals should clearly describe how the project is relevant to Edge AI in space, the thematic focus areas specified in this call for proposals, and contributes to sustainable growth.
Contribute to a sustainable system transformation
ESA Phi-Lab Sweden is committed to promoting sustainable development in the space sector in line with the global goals for sustainable development (Agenda 2030). The project application should discuss the societal and environmental impacts of the proposal. The environmental impacts of developing, using and maintaining AI systems can be significant and may in some cases outweigh the benefits.
In addition, AI solutions for space applications can facilitate disaster response through improved Earth observation, promote climate monitoring and environmental protection, foster innovation across industries, and promote global cooperation and security. Solutions can also drive economic growth by creating new markets and jobs, while ensuring sustainable practices and resource management.
We encourage project that strive to achieve the global goals with a particular focus on gender equality and climate change.
Given ESA Phi-Lab Swedens's focus on AI and edge learning, projects should conduct an early assessment of the sustainability of a given AI solution. This includes:
- Consider energy consumption during system development and favor models with lower complexity to reduce computational requirements.
- Evaluate greenhouse gas emissions from operations, with efforts to improve carbon efficiency and implement emission compensation strategies.
- Promote sustainable products and consumption patterns, reduce resource consumption and improve product quality and lifespan.
Regarding the spatial perspective in the AI project, it is particularly important to:
- Minimize waste, recycle materials and use renewable energy sources where possible.
- Ensuring that AI hardware designed for edge deployment remains energy efficient, including the use of specialized components such as hard processors and fault-tolerant designs.
- Investigate the possibility of recycling and reusing hardware, along with effective waste management scenarios to minimize environmental impact.
Agenda 2030 as a driver for innovation
Making scientific publications and results available
When results from research and innovation are made freely available, more people can contribute to solving societal challenges. This the call for proposals will help make results available to everyone. Therefore, all scientific publishing should be open access.