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Category: Philab

Towards a ‘Mission Control for Earth’: Better understanding Earth’s systems using AI and space data

Posted on by mavelar

In August 2025, FDL Earth Systems Lab presented three big AI research outcomes to improve how we understand and predict Earth’s changing systems and offer a window on how we might build a ‘Mission Control for Earth’. Leveraging the European Space Agency’s missions and funded by ESA Φ-lab, this initiative combines fresh datasets with innovative AI tools to give the global community better ways to track and respond to our planet’s most urgent environmental shifts.

“Guided by artificial intelligence, driven by human good”. This could be FDL Earth Systems Lab (ESL)’s motto. ESL is a research collaboration framework funded by ESA Φ-lab and implemented by Trillium Technologies, with the support of University of Oxford, Google Cloud, NVIDIA, Scan AI, and Pasteur ISI. It focuses on artificial intelligence (AI) – in particular machine learning (ML) – to support Earth sciences, helping researchers create practical tools for some of humanity’s toughest challenges with the best of motivations: ‘planetary stewardship’. 

FDL Earth Systems Lab has run annually since 2008. Experts with deep knowledge of the challenge domain work side by side with data scientists to develop new AI-enhanced approaches and tools. The short, focused format encourages quick testing and refinement, ensuring stronger results.

Last August, the ESL 2025 Live Showcase featured three ambitious research sprints: (1) refining 3D cloud models to improve forecasts of extreme events; (2) testing how well foundation models perform in sparsely observed events; and (3) advancing onboard ML to spot short-lived atmospheric events, such as greenhouse gas emissions. Each sprint brought together unique datasets and new AI-based methods to support the global research community.

Photo courtesy of Trillium Technologies.

3D CLOUDS FOR CLIMATE EXTREMES

Advancing global 3D cloud reconstruction is essential to deepen our understanding of cloud structure and the interactions with terrestrial and atmospheric phenomena. This is critical for tropical cyclones, which remain among the hardest weather systems to predict, especially during the intensification stage. Forecasts often poorly resolve a cyclone’s internal dynamics, simulations of cloud properties are highly uncertain, and observational records are limited, with only about 80 to 90 tropical cyclones occurring each year. The ‘3D Clouds for Climate Extremes’ sprint builds on a mature model training pipeline established in ESL 2024, which successfully modelled 3D clouds from geostationary data.

First, the team pre-trained a sensor-independent model on a large dataset of top-view satellite imagery from GOES-16, MSG and Himawari-8, to reconstruct masked versions of the observations. Second, they fine-tuned the model using a dataset from CloudSat, which provides vertical cloud profiles. The team also created a benchmark dataset, by combining satellite imagery with the timing and location of cyclone events. Since the model is sensor-independent, it is possible to include other satellite data that were not used for training, ensuring global coverage.

Together, these data enable the reconstruction of key microphysical properties of clouds, including ice water content (notably elevated in rapidly intensifying cyclones), droplet effective radius (a critical factor in cloud absorption and reflection of sunlight), and radar reflectivity (linked to cloud density and an indicator of rainfall).

Improving the prediction of cloud structures in three dimensions opens opportunities for a wide range of scientific and applied use cases: forecasts of hurricane intensity, discriminative cloud classification, or to understand how deforestation influences cloud cover and type. This ambition aligns closely with the objectives of ESA’s cloud, aerosol and radiation explorer mission,EarthCARE, which aims to advance our understanding of cloud-aerosol-radiation interactions.

FOUNDATION MODELS IN EXTREME ENVIRONMENTS

Earth observation foundation models are very powerful tools, but they also have limitations, especially when facing unfamiliar scenarios such as extreme events. One of the reasons is that training datasets typically contain limited examples from these events, leading to weaker performances when applied outside the conditions represented in the data.

When queried about a particular topic, foundation models can be ‘confidently wrong’. This becomes especially problematic when these models are used in critical, time-sensitive situations such as disaster response. It is essential to increase model transparency in cases where the model output has a high degree of uncertainty and requires human validation. But how can we know if the model is uncertain? 

The ‘Foundation Models for Extreme Environments’ team brought a novel answer to that question. The team – mentored by Φ-lab’s Internal Research Fellows Patrick Ebel and Ruben Cartuyvels – focused on distinguishing two types of uncertainty: data-driven or model-driven.

SHRUG-FM (Systematic Handling of Real-world Uncertainty for Geospatial Foundation Models) was developed as an adaptable framework for the community that combines input and training image comparison, embedding comparison, and the foundation model’s output and uncertainty into a planning and selective prediction mechanism, to ensure that the model can give a prediction, raise a warning, or simply say that it does not know the answer.

STARCOP2.0: ATMOSPHERIC ANOMALY DETECTION FROM ONBOARD

One of the most urgent applications of Earth observation is detecting and tracking greenhouse gas (GHG) emissions that are driving global warming. Methane, in particular, is one of the most powerful heat-trapping gases. Hyperspectral satellites play a crucial role in the detection of such gases: each gas interacts with light in a unique way, creating a distinct ‘spectral signature’ or ‘fingerprint’ that allows its identification from space.

The STARCOP 2.0 solution is built on a ‘tip-and-cue’ system that makes use of hyperspectral satellite data. In this setup, the ‘tip’ satellite is responsible for quickly detecting methane plumes. Once a plume is identified, it alerts the ‘cue’ satellite, which carries out more advanced tasks such as detailed plume segmentation and estimating methane concentrations using a U-Net ML model.

Unlike traditional approaches, image analysis happens directly onboard, avoiding delays from sending images to ground stations for processing. To achieve this, the team built two ML-ready datasets, one with orthorectified images, and another with un-orthorectified images that are more suitable and realistic for onboard implementation. These datasets were used to train three models, bypassing the need for image correction and reducing inference time.

The datasets have been shared with the community, and the models are being optimised for spacecraft limitations in computing power, memory and energy. This system makes it possible to detect methane and other GHG leaks quickly, helping policymakers hold polluters accountable and support efforts to reduce emissions.

“We’re motivated to show how AI’s powerful predictive and insight-extracting toolbox can make a significant difference to how we monitor and manage our planet. What’s exciting about this year’s research products is that we are showing how multi-instrument methods and context-aware AI can be harnessed to make a dent in open problems – such as rapidly determining the anatomy of a cyclone or identifying erroneous greenhouse gas emissions from orbit. If you are a tech optimist – which we are – you will see that the puzzle pieces for a ‘mission control for Earth’ are now within our reach,” commented James Parr, Founder and Chief Executive Officer at Trillium Technologies.  

Nicolas Longépé, Earth Observation Data Scientist at Φ-lab, is ESA’s Technical Officer for the initiative: “The FDL sprint format works because it brings together experts from different fields to collaborate intensively and prototype solutions quickly. By combining domain specialists, AI researchers, and technical mentors, we can tackle complex, carefully chosen challenges with real impact. These three sprints fit perfectly into the Earth Action paradigm we pursue at Φ-lab, moving beyond passive observation towards proactive insights and decision-making for a more resilient planet.”

To know more: ESA Φ-lab, Trillium Technologies, FDL ESL AI SOTA Live Showcase

Photo courtesy of Trillium Technologies

Advancing AI for Earth observation at the REO workshop

Posted on by mavelar

The first ‘REO: Advances in Representation Learning for Earth Observation’ workshop will bring together researchers and practitioners from machine learning, computer vision, and Earth sciences to advance the development of robust, interpretable, and scalable models for monitoring our planet. The ‘Call for Papers’ submission deadline is 20 October 2025.

(Updated on 15 October 2025)

Taking place at the Bella Center Copenhagen on 6/7 December 2025, the Representation Learning for Earth Observation (REO) workshop – part of EurIPS, a European conference officially endorsed by NeurIPS – will gather experts from machine learning, computer vision, and Earth sciences to present the latest research, discuss real-world scientific uses, and share innovative system designs.

With massive, diverse datasets from satellites and other sensors becoming widely available – and with the rise of general-purpose foundation models – Earth observation faces new opportunities and complex challenges. But how can we best combine these various streams of information to create useful applications?

The development of representation learning algorithms that understand raw Earth observation data with minimal human instruction is gaining traction beyond university labs. This interest is highlighted by projects from technology leaders such as Google DeepMind’s AlphaEarth, ESA-IBM’s TerraMind, AllenAI’s Earth System, or Meta’s DINOv3.

This growth calls for more focused discussions on how to develop, deploy, and use these powerful models. The workshop will address fundamental questions like “Where is the field today, and what steps should the community take next?”, “What are the biggest hurdles in getting computers to effectively learn from Earth data?” or, given the trend towards general-purpose, one-for-all AI models, “What is the role of specialised approaches for Earth science?”

Participants are invited to present their novel work as extended abstracts or discuss recently published work that is relevant to the workshop. The ‘Call for Papers’ submission deadline is 20 October 2025. While the current deadline is set, organisers advise potential contributors to check the workshop’s website for any possible updates.

The topics are broad and exciting, including new approaches in machine learning for Earth observation, such as self-supervised, multimodal, and domain-adaptive models. Experts will discuss the combination of AI with physics, and the integration of established models into AI pipelines to get better predictions and understand the uncertainty in their results.

A major focus is on ecology and environmental monitoring, covering essential tasks like tracking changes in land use, mapping biodiversity, estimating forest biomass, and assessing the conditions of soil and vegetation.

Technical discussions will also focus on the difficulties of remote sensing data processing, such as combining different types of sensors and ensuring consistency between different satellites.

Discussions on data curation and accessibility will cover how to build fair, accurate, and easily accessible global datasets for research, ultimately driving real-world innovations in applications like mapping urban areas or monitoring natural disasters.

Leading scientists and industry experts will give keynote presentations: Gustau Camps-Valls from the IPL lab of the University of Valencia, Michal Kazmierski from Google DeepMind, Julia Gottfriedsen from OroraTech, Bertrand Le Saux from the European Commission, and Ankit Kariryaa from the University of Copenhagen.

“REO will provide an amazing opportunity for machine learning researchers and practitioners that are interested in Earth observation to find each other in Europe,” commented Ruben Cartuyvels, Internal Research Fellow at ESA Φ-lab. “The current trend in AI4EO of representation learning with foundation models of increasing size leaves many open questions, and fruitful community exchange is essential to take steps towards answering those”.

Interested parties can find out more about this workshop and submit their abstract here.

This workshop is being co-organised by researchers from ESA Φ-lab, the University of Copenhagen, the Technical University of Berlin, IBM Research Europe and ENPC.

To know more: REO Workshop

The banner image contains modified Copernicus Sentinel data (2024), processed by ESA

Leadership and technology: AEE boosts Spain’s resilience against wildfires

Posted on by mavelar

Given the scale of the recent wildfires that have significantly affected Spain’s land and ecosystems, the Spanish Space Agency (AEE) has taken the initiative to strengthen the country’s capabilities in prevention, detection, and response. The objective is clear: to turn the challenge into an opportunity to bolster prevention, detection, and response capabilities, ensuring that Spain has the most advanced tools to protect lives, infrastructure, and the environment.

In collaboration with the European Space Agency (ESA), through its ESA InCubed programme, AEE is launching a pioneering national call for the development of innovative Earth Observation applications.

Read the full article (in Spanish) on www.aee.gob.es.

Improving precision nitrogen management with Messium

Posted on by mavelar

Messium is improving agricultural practices with advanced hyperspectral satellite data. Co-funded by the ESA InCubed programme, the company developed a tool that provides farmers with frequent, accurate insights into crop nitrogen levels and optimal fertiliser use, helping to boost yields, cut costs, and minimise environmental impacts.

Nitrogen is one of the most essential nutrients for crop growth, playing a central role in plant development, yield, and quality. In that sense, nitrogen fertilisers are crucial for boosting land productivity and sustaining global food demands.

However, the average global Nitrogen Use Efficiency (NUE) on crops is around 45%, with more than half of the applied nitrogen fertiliser lost as nitrous oxide emissions or leached in the form of nitrate into drinking water sources, contributing to groundwater contamination and surface water eutrophication.

Behind this nitrogen loss is the imprecise nature of fertiliser application. Farmers often apply nitrogen at incorrect amounts and times, a practice driven by a lack of real-time data on a crop’s specific needs. To improve both the sustainability and profitability of modern farming, a shift is required – one that moves away from relying on broad, imprecise fertiliser application toward more targeted, data-driven approaches.

Messium, a UK-based start-up, emerges as key player in precision nitrogen management, by using hyperspectral satellite data and artificial intelligence to assess the nitrogen status of wheat crops and address sub-optimal nitrogen use in farming – something that was not possible with previous multispectral/NDVI-based approaches. Nitrogen, like all chemical elements on Earth, reflects and absorbs radiation in a specific set of wavelengths, creating a unique spectral signature – a ‘fingerprint’ – that is identified by hyperspectral satellite technology.

Messium’s methodology is built on real-world data: 20000 geo-referenced samples from wheat crops were collected, matching the collection with hyperspectral satellite imagery. These samples were then analysed to get precise measurements of nitrogen and biomass. Together, this information was used to train Messium’s unique machine learning models, giving them the ability to accurately analyse crop health from above.

Messium makes crop growth models a viable tool for farmers: the company’s innovative hyperspectral solution provides real-time, in-season data on a crop’s nitrogen percentage and biomass, filling the critical data gap that previously rendered these models unusable for decision-making. Messium integrates this live information with weather, soil, and farm management data to create a comprehensive picture of crop health and nutrient needs. From this, growth models predict the crop’s maximum and most profitable yields, calculate the precise amount of fertiliser required, and can even forecast changes in crop status.

Another key point of Messium’s approach is the nitrogen dilution curve, which maps a crop’s nitrogen percentage against its biomass to determine if it has a nitrogen surplus or deficiency, indicating the ideal time for fertilisation. By combining the timing insights from the dilution curve with the optimal quantity from growth models, Messium optimises the amount of fertiliser and timing of application, increasing the average NUE to 80-85%.

The company follows a B2B2F (business-to-business-to-farmer) model: it provides fertiliser companies and precision agri-tech start-ups with nitrogen estimation insights that are seamlessly integrated into their platforms. Then, these partners deliver Messium’s data to end-user farmers and agronomists through their established networks, allowing weekly, more precise fertiliser recommendations without requiring any behavioural changes.

Messium became one of the leading players in the use of hyperspectral technology for agriculture: last year’s trials across Europe and Australia, using more than 13,000 lab-validated crop samples, found that over 50% of fields were incorrectly fertilised, leading to wasted input costs and unnecessary emissions. Messium’s technology enables a data-driven approach to tackle these inefficiencies, supporting commercial farmers as well as broader food security and net-zero objectives.

“At a time when Europe’s food security and sovereignty are more vital than ever, optimising nitrogen fertiliser use is key. At the same time, tackling harmful nitrous oxide emissions and nitrate leaching is essential to reaching net zero,” commented Vishal Soomaney, co-founder and CTO of Messium.

This start-up has achieved remarkable success through its own innovative approach, with the ESA InCubed programme providing valuable technical support and co-funding that helped accelerate its growth. Having reached a minimum viable product with InCubed in February 2025, Messium has secured £3.2 million in private investment and is now starting an extension of its product with InCubed in 2026.

Its success does not stop there: Messium has been collaborating with Open Cosmos – another InCubed-supported company – as a user of the HAMMER hyperspectral datasets, highlighting the importance of the InCubed ecosystem to find new customers, strategic opportunities, and valuable peer-to-peer feedback.

“With ESA InCubed’s support, we’ve turned Messium from a proof-of-concept into a commercial solution that helps farmers boost profits, cut emissions, and protect soil health for future generations. This collaboration has fostered strong partnerships with organisations like Open Cosmos and shown the real-world impact of space-enabled innovation. We’re excited to continue working with the ESA team to scale these solutions across Europe”, added Vishal.

Crop nitrogen (left, in %) and biomass (right, in kg/ha) in a field under analysis. During the season, the percentage of nitrogen in the crops can vary from 6 to 1%, and crop biomass can go as high as 16 t/ha. Messium’s in-depth analysis of a field at any point in the season allows for better nitrogen management. Credits: Messium analysis of Open Cosmos hyperspectral data.

Michele Castorina, Head of the Φ-lab Invest Office and InCubed Programme Manager, commented: “The collaboration between these two InCubed-supported companies is a clear indication of the programme’s success. InCubed cultivates an ecosystem where these ideas can connect, grow, and create new commercial value. By enabling the development of Messium’s product, we have demonstrated how European space technology can be transformed into a viable business proposition. Their solution is a perfect example of the innovative synergy we foster, showing how InCubed’s support further attracts significant investment needed to scale.”

“At Open Cosmos, our mission is to tackle Earth’s most pressing challenges with actionable data and connectivity from space,” stated Alberto Perez Cassinelli, Vice President of Data at Open Cosmos. “By providing Messium with our advanced hyperspectral data from our OpenConstellation, we are empowering their nitrogen analysis technology to deliver real value to wheat farmers worldwide. This collaboration demonstrates how space-based innovation can translate into practical, real-time insights that improve agricultural efficiency, sustainability, and food security.”

The banner image shows a NDVI-based approach from multispectral satellite imagery previously used by farmers to assess the status of their crops (left) vs. crop nitrogen (kg/ha) provided by Messium, based on hyperspectral satellite imagery (right). In the right image, lower crop nitrogen levels are represented in red and higher crop nitrogen levels in blue. Messium’s weekly insights provide the nitrogen percentage in the crop, the biomass of the crop (t/ha), and the total nitrogen in the crop (t/ha), all at a 5 x 5 m resolution.

To know more: ESA Φ-lab, Messium

Photo courtesy of Messium

A bold new chapter for AI4EO with ‘ESA Φ-lab Challenges’

Posted on by mavelar

With a new look and the same ambition, the rebranded ‘ESA Φ-lab Challenges’ return with a fresh momentum, inspiring the Earth observation and AI communities to drive innovation through a new series of impactful competitions.

Earth observation (EO) is a powerful window that shows how the physical, chemical, and biological systems of our planet are interconnected. Until now, initiatives like ESA’s AI4EO have demonstrated how the combination of remote sensing technologies and AI can reveal hidden patterns and drive environmental, technological, and social innovation at a larger scale – from measuring biodiversity and soil health, to urban city planning or disaster response.

But to keep up with the evolving nature of innovation, we must also broaden our approach. This is why AI4EO is evolving into ‘ESA Φ-lab Challenges’. While AI is a fundamental technology, it is just a piece of a bigger puzzle. This new initiative will open the door to a wider range of cutting-edge technologies and approaches, encouraging innovation in all areas of Earth observation.

At the same time, these challenges are a platform for researchers and innovators to showcase their work, contribute with practical solutions to shared global issues, and help build a dynamic and engaged Φ-lab community.

Ready to make a difference? Here are three challenges you cannot miss:

From orbit to action: AI for Earthquake Response

    What if you could help first responders and support life-saving decisions right after an earthquake?

    Earthquakes are among one of the most unpredictable and destructive natural disasters, capable of destroying buildings, severing power lines, and bringing entire cities to a standstill in seconds. In the aftermath, time is critical and swift action is needed to contain further destruction, rescue survivors and restore order.

    Despite having access to terabytes of high-resolution satellite imagery, mapping affected areas still relies heavily on human interpretation, making it a time-consuming task when there is no time to lose. Together with Earth observation data, artificial intelligence emerges as a promising tool to automate and potentially accelerate disaster response.

    To support humanitarian and disaster relief efforts, ESA Φ-lab and the International Charter ‘Space and Major Disasters’ invite data scientists, AI researchers, students, geologists and developers around the world – solo or in a team – to join the ‘AI For Earthquake Response’ challenge.

    This initiative challenges you to develop state-of-the-art AI models that will automatically detect damaged vs. undamaged buildings, by analysing pre- and post-event satellite imagery. Participants will have exclusive access to a curated archive of multi-mission, high-resolution satellite imagery collected from previous Charter activations. All the EO data products of the virtual constellation used in past Charter activations concerning earthquakes are seamlessly ingested and processed by the on-line platform ‘Charter Mapper’, and made available through the Earth Observation Training Data Lab (EOTDL).

    This challenge foresees two main phases: one ‘training and live scoring phase’, where participants will have the possibility to train and test their models on partially annotated scenes (closing on 5 September), and a ‘stress test phase’, where participants will have to deal with fully annotated imagery from previously unforeseen sites, like in a real earthquake scenario.

    A webinar about this challenge is available here. The deadline is 15 September 2025, 17:00 CEST. Winning models will gain visibility in open-science forums and may be considered for integration into the ESA Charter Mapper, potentially becoming tools used by the Charter community in future disaster response activations. The first, second and third place will be awarded € 3000, € 2000 and € 1000, respectively, during the 54th Charter Meeting, from 6 to 10 October 2025 in Strasbourg, France.

    HYPERVIEW2: explainable artificial intelligence

      Earth observation is transforming agricultural practices by providing timely, large-scale insights into crop health, soil conditions, water availability and land use/land cover. As these Earth observation systems rely increasingly on AI to process vast amounts of data, it is essential that the models used are not only accurate but also explainable.

      Explainable AI (XAI) ensures that farmers, agronomists, and decision makers can understand and trust the reasoning behind these outputs. This transparency is key to building confidence in digital tools, allowing for their responsible and effective use in agriculture.

      Following the success of the HYPERVIEW challenge in 2022, the HYPERVIEW2 challenge is now back to develop new XAI systems applied to agriculture, using airborne hyperspectral images, Sentinel-2 multispectral images and PRISMA hyperspectral images.

      The goal is to develop an XAI model to estimate the concentration of six important contaminants/trace elements in soils – Boron (B), Copper (Cu), Zinc (Zn), Iron (Fe), Sulphur (S) and Manganese (Mn) – using Earth observation imagery. In the right balance, these elements boost plant health, productivity, and resilience to stress – important information that farmers need to optimise crop nutrition and yield.

      This challenge was launched by Φ-lab, together with KP Labs, the Warsaw University of Technology, and the Poznan University of Technology. The deadline for applications is 14 September 2025 and the award ceremony will take place at the EASi Workshop, during the European Conference on Artificial Intelligence, from 25 to 30 October in Bologna, Italy.

      PANGAEA: testing geospatial foundation models’ capabilities with a cutting-edge benchmark dataset

        If you want to dive deeper into benchmarking or tackle targeted geospatial tasks, the PANGAEA challenge will be the right one for you.

        PANGAEA is a highly curated, comprehensive benchmark dataset for Earth observation, designed to evaluate the performance of machine learning models across a broad range of geospatial tasks, such as land cover classification, change detection, environmental monitoring, and multi-sensor/multi-temporal analysis, among others.

        What makes it so unique is its diversity and structure: while it covers a wide spectrum of resolutions, sensor types, and temporal layers, it also provides a standardised protocol for evaluating the performance of a model, which is crucial for comparing results from different researchers, institutions, and AI approaches. Additionally, PANGAEA is designed to test and refine geospatial Foundation Models, a new generation of AI models with a wide range of applications across Earth observation.

        This will be an open-ended challenge: participants will have the opportunity to continuously explore, experiment, and iterate their models over time. Within this challenge, there will be regular Data Sprints: short, high-intensity mini-challenges that will focus on specific real-world tasks using the PANGAEA dataset, with clear goals and metrics, and their own prize pool and recognition opportunities. These are ideal for teams looking to make a mark, try something new, or just have fun competing under pressure.

        The community should stand ready: the next Data Sprint will be announced later in 2025, promising fresh challenges, new opportunities, and a chance to shine.

        You can know more about these three challenges here.

        ‘ESA Φ-lab Challenges’ is an initiative created by ESA Φ-lab and implemented by Novaspace, Planetek Italia, Sinergise, GMATICS, and EarthPulse.

        To know more: ESA Φ-lab, Φ-lab Challenges

        Photo courtesy of ESA Φ-lab Challenges

        Help ESA redefine the future of space computing

        Posted on by mavelar

        Due to the growing volume of data produced by Earth observation (EO), traditional computing architectures struggle to process information efficiently and promptly. To mitigate this issue, prepare Europe for the future of space computing, and grow from Earth observation into Earth action, ESA is seeking innovative mission concepts that use disruptive computing paradigms, potentially coupled with matching sensing technologies that could either bring new capabilities for Earth-orbiting satellites, or significantly improve current mission constrains. 

        Artificial intelligence (AI) and novel computing paradigms such as quantum, photonic or neuromorphic computing have the potential to transform space-based applications by dramatically increasing mission autonomy and decision making without humans. To consolidate Europe’s position as a leader in sustainability and remote sensing, ESA is launching the new SysNova challenge “Innovative mission concepts enabled by disruptive computing paradigms“.

        The call builds on multiple past and ongoing initiatives at ESA. “Through missions like Φ-sat, OPS-SAT, and initiatives such as 3CS, ESA has explored the benefits of embedding intelligence in orbit. In parallel, disruptive paradigms like quantum and neuromorphic computing have shown potential to enhance processing of vast amount of data efficiently. Yet, few have examined how these technologies could redefine entire missions. It’s time to take that next step”, says Gabriele Meoni, Innovation Officer at ESA Φ-lab and one of the campaign managers.

        Read the full article on www.esa.int.

        Living Planet Symposium Extra News: Day 5

        Posted on by mavelar

        ESA’s Living Planet Symposium came to a close today, concluding a week of networking, discussions and meeting of curious, scientific minds.   

        Today, one of the focal points was thermal imaging instruments, which are critical for monitoring land-surface temperature – and will be carried on upcoming missions such as the upcoming Copernicus Land Surface Temperature Mission. ESA’s Soil Moisture and Ocean Salinity (SMOS) mission celebrated passing its 15-year milestone in orbit – the mission has helped improve weather and climate models.

        Three new contracts were signed for ESA’s InCubed programme, which is central to the agency’s efforts to turn promising concepts into successful Earth observation services, strengthening Europe’s position in this rapidly evolving sector. 

        Read the full article on www.esa.int.

        Unmissable Φ-lab moments at LPS 2025

        Posted on by mavelar

        At this year’s Living Planet Symposium, ESA Φ-lab will present its innovations and initiatives at the forefront of Earth Observation. Make sure not to miss our key moments.

        The European Space Agency’s Living Planet Symposium (LPS) is one of the world’s largest events dedicated to Earth Observation (EO). LPS25 – this year’s edition – will take place from 23 to 27 June, in Vienna (Austria), gathering scientists, policymakers, and industry experts to share the latest research, satellite-based applications, and innovative technologies addressing environmental and societal challenges.

        As part of this dynamic programme, ESA Φ-lab will be actively involved, presenting its next-generation solutions at the intersection of EO, transformational innovation, commercialisation for human prosperity, climate action, and sustainability, among others.

        Curious about Φ-lab? Make sure to pass by the Φ-lab corner at ESA’s stand (Main Hall, ground floor) to meet the team, discover how Φ-lab drives cutting-edge research and disruptive technologies in Earth Observation, and learn about opportunities for collaboration.

        Here are Φ-lab’s must-sees:

        1. The future of geospatial data discovery and use

        The rapid growth of EO data availability calls for new approaches to efficiently manage, analyse and extract meaningful insights from heterogenous and enormous satellite datasets. In this context, self-supervised learning and foundation models are emerging as transformative tools, offering unprecedented capabilities for detecting patterns, changes, and anomalies across the planet.

        Φ-lab has built a strong expertise in using powerful AI models. The latest example is the joint ESA/IBM Research Europe release of TerraMind, a next-generation geospatial foundation model designed to help us better understand and protect our planet.

        AI is reshaping EO, enhancing data analysis, discovery, and interaction through multimodal data and language models. In the session “AI and Earth observation – where to now?”, Φ-lab Visiting Professors will share the latest advances and lead a thought-provoking debate on the future of AI in remote sensing.

        During the session “Foundation Models for Earth Observation: Current solutions with less labelled data to improve environment monitoring and future perspectives to revolutionize geospatial data discovery and utilization”, you will learn about three of the latest Φ-lab-supported projects on the topic of foundation models for EO: TerraMind, FM4CS (Foundation Models for Climate and Society) and PhilEO.

        EVE (Earth Virtual Expert) is a large language model (LLM) developed to support the EO and Earth Science communities. It builds on open-source LLMs, trained on billions of curated EO data tokens and fine-tuned with tailored datasets. Designed to assist both expert and non-specialists, EVE makes complex EO knowledge accessible to everyone through natural language processing. In the session “EVE: A Comprehensive Suite of LLMs and Data for Earth Observation and Earth Sciences”, attendees will discover how EVE was built, explore its capabilities, and learn how to interact with it for their own applications.

        Ensuring AI technologies are explainable, trustworthy, and physics-aware is essential. “Explainable AI for Earth Observation and Earth Science” will explore cutting-edge advancements in Explainable AI methods across diverse data types, including SAR, optical, and hyperspectral data. Attendees will discover innovative strategies to bridge data gaps, address physical inconsistencies, and promote responsible, ethical AI use in support of Earth Action initiatives.

        2. Advancing weather and climate forecasting with Machine Learning

        The monitoring and prediction of Earth’s weather and climate systems have seen remarkable progress in recent years. With the growing availability of high-resolution satellite data and sophisticated in situ sensors, we have now access to an unprecedented amount of data about our planet’s interconnected systems. Machine learning and deep learning techniques are transforming the way we interpret, model, and forecast the complex dynamics of Earth.

        “Machine Learning for Earth System Observation and Prediction” will bring together researchers exploring the latest AI-driven approaches in environmental science. It will highlight innovations in data assimilation, climate prediction, and the development of large-scale, data-driven Earth system models.

        3. Investing in commercial ideas that change the way we see our planet

        Great ideas need more than ambition. They need backing, and this is where the ESA InCubed programme steps in. By blending co-funding, technical expertise, and commercial and industrial guidance, InCubed is a key tool of ESA’s EO commercialisation strategy to effectively bridge the gap between vision and commercial success in the EO sector.

        As the demand for agile EO solutions grows, public-private partnerships are emerging as a powerful model to accelerate innovation and optimise resources. “New approaches to support commercialisation” will gather industry and institutional voices to explore the opportunities of new approaches, but also address challenges such as goal alignment, intellectual property, and data access.

        “Enhancement of EO products using advanced multi-instrument and multi-platform synergies” will focus on methods that exploit synergies between complementary observations, modelling and multi-sensor data, using data from missions like Copernicus’ Sentinels, EarthCARE, MTG, EPS-SG, PACE, among others.

        “Commercial Earth Observation Missions: Embracing New Paradigms and Innovative Models” will explore new commercial EO mission concepts designed to meet both institutional and market needs. With examples of business models – from public-private partnerships to fully private ventures – this session will offer insights into the evolving commercial EO landscape.

        Driven by new climate regulations such as EU ETS and CBAM, the demand for accurate GHG monitoring is rising. “Opportunities in the Earth Observation Market: A Focus on GHG Monitoring” will focus on the commercial opportunities at the intersection of Earth Observation and climate policy, featuring an overview of the regulatory landscape, and a panel discussion with EO companies developing services to meet emerging compliance needs.

        4. Harnessing Quantum Computing for a smarter, greener future

        Quantum Computing (QC) promises to process vast amounts of information more efficiently than classical systems, creating new opportunities for climate modelling, environmental monitoring, and the analysis of highly complex, interconnected natural systems. By accelerating data processing and enabling new types of simulations, quantum technologies could improve the accuracy of climate predictions and support more responsive decision making in the face of global challenges.

        In the “HPC and Quantum Computing” Insight Session, experts will discuss how quantum technologies are beginning to transform the way we work with EO data. It will also explore hybrid quantum-classical approaches, which combine the strengths of both computing paradigms.

        “Joint ESA-GRSS initiatives for the exploitation of Earth Observation data” will focus on the work developed by the Quantum Computing for Earth Observation Working Group, an initiative that is part of the IEEE-GRSS Technical Committee QUEST (Quantum Earth Science and Technology) and operated in collaboration with Φ-lab. This project fosters collaboration between the QC and EO communities and is working to turn the promise of QC into practical, impactful applications for EO, through joint research, open knowledge exchange and hands-on projects.

        5. Smarter satellites, faster insights: inside the Φsat-2 mission

        Designed to test new mission concepts and onboard data processing using advanced AI processors, ESA’s Φsat-2 is an innovative nanosatellite that runs multiple applications directly in orbit. Equipped with a high-resolution multispectral instrument, it supports tasks like cloud detection, vessel classification, wildfire monitoring and image compression.

        “The ESA Φsat-2 mission: an AI empowered 6U Cubesat for Earth Observation” session will present the mission’s status, AI demonstrations, and opportunities for the community to engage. Make sure not to miss the Φ-lab-led presentation: “All4One or One4All? Tailoring Onboard AI with NAS and Foundation Models”.

        6. Where future careers in Earth Observation begin

        To secure the future of Earth Observation, it is essential to engage and inspire young professionals today. The “Exploring Space Opportunities with ESA Φ-lab and EUSPA: Pathways for Students and Young Professionals” session is designed for students and young professionals eager to enter the European space sector, with a focus on innovation, technology, and entrepreneurship.

        The session will introduce two key institutions: ESA Φ-lab and EUSPA, the European Union Agency for the Space Programme, which manages operational EU space services like Galileo, EGNOS, and Copernicus.

        The Grand Marathon, organised by Φ-lab, is an innovation challenge rewarding scalable, market-ready solutions addressing climate events and infectious diseases, with a focus on younger populations. Launched in November 2024 and held in partnership with Save The Children and Hello Tomorrow, the competition celebrates the power of AI-based technologies and blockchain for global resilience.

        The “Grand Marathon Finalists pitching and award” session will host the final pitch between the two top teams – GEOMATYS and Plastic-i – who will receive € 50.000 each and compete for the € 150.000 first prize.

        Join Φ-lab at LPS25 and take the opportunity to connect, explore new possibilities, and be part of the conversation driving the next wave of Earth Observation innovation.

        To know more: Living Planet Symposium, ESA Φ-lab, TerraMind, FM4CS, PhilEO, EVE, InCubed, QUEST, Φsat-2

        Photo courtesy of ESA

        Join ‘Call for Φdeas’ and make your transformative mark in Earth Observation

        Posted on by mavelar

        ‘Call for Φdeas’ is a call for ideas sponsored by ESA Φ-lab to stimulate transformative innovation in the Earth observation sector. This call encourages the submission of groundbreaking ideas that can make an impact in scientific fields like Earth Science, green-tech, climate-tech, and sustainability, institutions, NGOs or in the commercial sector. Selected ideas can receive up to € 1.000.000 in funding and the deadline for submissions is 31 August 2025.

        In a rapidly evolving world, Earth observation (EO) plays a vital role in understanding and addressing global and local challenges. To keep pace with emerging needs and technological advancements, it is essential to look for fresh perspectives and novel approaches. Funding calls such as ESA Φ-lab’s ‘Call for Φdeas’ create valuable opportunities to explore untapped potential for transformative innovation in the Earth Observation domain.

        ‘Call for Φdeas’ is open to research and academic institutions, NGOs, commercial entities (start-ups, SMEs and LSIs), international collaborators, among others, to propose ambitious, forward-thinking initiatives that will have an impact in scientific fieldslike Earth Science, green-tech, climate-tech, and sustainability, institutions, NGOs or in the commercial sector.

        The main targets for this call are ideas with transformative potential, not incremental innovation. Selected ideas are expected to deliver significant technology progress and/or impact the reference sector/market/system by a different use of current technologies.

        Selected ideas can be used to populate future ESA Φ-lab workplans or as input for other ESA programmes (e.g., FutureEO, InCubed). ‘Call for Φdeas’ offers a maximum funding of € 1.000.000 per idea and co-funding is encouraged. For more information, please refer to ‘Evaluation Criteria’ in the dedicated Call for Φdeas channel on the Open Space Innovation Platform (OSIP).

        Ideas should fall into three categories: ‘Exploratory Ideas’ (to investigate novel, unconventional or unproven EO-related concepts, including technologies, mission studies or EO applications), ‘Capacity Building’ (to build the competences, techniques, or ecosystems needed to mature promising disruptive EO ideas), and ‘Innovation Impact’ (to translate a mature idea into a transformative solution ready for adoption for an identified use case).

        This is a recurrent call and accepts new submissions twice a year. For 2025, a single round of submissions is foreseen, and the submission phase deadline is 31 August 2025 COB.

        By encouraging diversity of ideas, ‘Call for Φdeas’ helps ensure that the Earth observation sector remains dynamic, relevant, and responsive to the complex realities of our planet.

        To know more: ‘Call for Φdeas’ OSIP, ESA Φ-lab

        Photo courtesy of ESA

        Strengthening space ties: new ESA International Fellowship for a Brazilian researcher

        Posted on by mavelar

        The European Space Agency (ESA) and Brazil have maintained a collaborative relationship in space exploration and technology over the years. In 2002, ESA and Brazil signed a Framework Cooperation Agreement to expand joint efforts in space science, Earth Observation, telecommunications, microgravity experiments, and life sciences, facilitating the exchange of experts and collaborative studies, and strengthening the scientific and technical ties between the two entities.

        Since then, other joint initiatives followed. In 2011, Brazil’s National Institute for Space Research (INPE) became a member of the International Charter ‘Space and Major Disasters’, an ESA co-founded global initiative that provides rapid satellite data access to support disaster management efforts. In 2018, ESA and the Brazilian Space Agency (AEB) signed an Implementing Arrangement to establish and use telemetry and tracking facilities in Natal, Brazil.

        In March 2024, Simonetta Cheli, Director of Earth Observation Programmes at ESA, and Clezio Marcos De Nardin, Director of the National Institute for Space Research, signed a Protocol of Intent to strengthen the relationship between ESA and INPE/AEB.

        As part of the research outcomes stemming from the Protocol of Intent signed between ESA and INPE, Gabriel da Rocha Bragion, ESA International Research Fellow, will spend 12 months at ESA Φ-lab developing methods for estimating carbon stocks from biomass, using SAR data and machine learning techniques. 

        Read the full article on www.eo4society.esa.int.