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Category: Philab

Earth observation community spotlight: Saturnalia

Posted on by mavelar

In this article, we speak with Gianni Iannelli, Chief Executive of Saturnalia, about the challenges facing agriculture, the importance of Earth observation in crop monitoring and risk assessment, and how data provided through the TPM programme are helping Saturnalia achieve its aims.

Italy-based Saturnalia is a geospatial intelligence company that harnesses the power of Earth observation data to improve decision-making and risk management in agriculture. The company offers an easy-to-use platform that is used by farmers and agriculture insurers to better protect crops, assess exposure, and enhance productivity.

Saturnalia works with data from ESA’s Third Party Missions (TPM) programme, which disseminates data from commercial and institutional partners to European businesses participating in ESA incubation activities or developing pre-commercial applications.

Saturnalia gained access to these datasets through an ESA InCubed project, which was instrumental in enabling the development of our AI-driven processing pipeline. As part of this work, we integrated data provided by ESA TPM to prove the feasibility of the solution without incurring prohibitive upfront costs.

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

Photo courtesy of Unsplash/Ant Rozetsky

Join us for the 2026 ESA EO Commercialisation Forum

Posted on by mavelar

Held in Seville (Spain) from 12 to 14 May 2026, the 3rd ESA Earth Observation Commercialisation Forum will give attendees the opportunity to meet institutions, industry leaders, start-ups, investors, users and entrepreneurs, and connect with potential partners while staying ahead of key Earth observation market trends and challenges.

The 3rd ESA Earth Observation Commercialisation Forum (ESA CommEO) will take place from 12 to 14 May 2026 at the prestigious Hotel Meliá Lebreros (Seville, Spain). Organised by ESA Φ-lab and supported by the Spanish Space Agency, the event will bring together the global Earth observation ecosystem for three days of insight, innovation, and high-level networking.

This years’ edition is focused on the latest trends arising in the Earth observation commercial market, featuring an engaging programme that includes keynote speeches, panel discussions, exhibitor booths, and curated matchmaking opportunities designed to foster new partnerships and boost commercial growth within the Earth observation sector.

The programme is divided into three key axes – ‘Strategy, Finance & Market Dynamics’, ‘Earth Intelligence, AI & Commercial Adoption’ and ‘Key Verticals & Future Capabilities’, creating a well-rounded experience that caters to diverse interests, expertise levels and strategic priorities.

For the first time, the event will offer dedicated sponsorship opportunities, giving organisations the chance to strengthen relationships with end users, institutions, entrepreneurs, and investors. Sponsors will also be able to generate qualified leads by connecting directly with key stakeholders who are actively shaping the future of Earth observation commercialisation.

The event will also feature various parallel sessions, from matchmaking with investors and exploring commercial opportunities in Africa, to supporting New Space companies and exploring Copernicus Contributing Missions.

While the event has a strong focus on commercialisation, it also supports innovation. The top three finalists of the ESA Φ-lab Grand Marathon will pitch their Earth observation-based solutions aimed at protecting civilians in disaster and public-safety contexts.

For the third year, ESA CommEO will give start-ups the opportunity to compete for the CommEO Award. Powered by ESA and Creative Destruction Lab (CDL-Milan), the 3rd ESA CommEO Award is designed for ambitious, early-stage startups looking to anchor their technical innovation in a robust commercial strategy. Prizes include a guaranteed interview for the Creative Destruction Lab’s Global CDL Space Programme, a € 25.000 voucher for ESA Third Party Missions (TPM) data, a € 10.000 voucher for OVHcloud’s cloud computing services, and a free admission to this event.

A great event goes beyond keynote speeches and panel discussions: attendees will have the opportunity to network during the event’s Gala Dinner and wander through the Seville’s grand plazas during the event’s social activity.

More information about the event and registrations is available on the ESA CommEO website.

To know more: ESA CommEO, ESA Φ-lab, Spanish Space Agency, Creative Destruction Lab (CDL-Milan)

Photo courtesy of ESA

Using Φ-lab’s machine learning algorithms to fight mosquito-borne outbreaks in Brazil and Peru

Posted on by mavelar

As the number of dengue and malaria cases rises each year, governments and health authorities are in a race against time. DIRE (Disease Incidence and Resource Estimator) is a digital, predictive data analysis and visualisation platform that transforms climate and epidemiological data into a concrete operational roadmap by using a machine learning approach developed by ESA Φ-lab for UNICEF. This platform will help governments in high-burden regions like Brazil and Peru to shift from reactive crisis management to proactive, life-saving preparation.

Dengue and malaria are two of the most threatening mosquito-borne diseases worldwide, placing an immense burden on global healthcare systems and economies. According to the World Health Organization (WHO), about half of the world’s population is now at risk of dengue, with an estimated 100 to 400 million infections occurring each year.

As for malaria, it remains a leading cause of mortality, particularly among children under five years old in sub-Saharan Africa. The World Malaria Report from 2024 states that, in 2023, there were an estimated 263 million cases and 597 000 deaths globally.

While these two diseases are transmitted by different mosquito species, the causes that lead to their spreading within populations are very similar. Dengue and malaria are both deeply tethered to the environment. Climate change, land use change, deforestation, rapid urbanisation and poor drainage create ‘hotspots’ where mosquitoes thrive, increasing human exposure.

When we talk about infectious diseases, timing is everything. Tools that predict outbreaks are therefore paramount to shift public health action from reactive – responding once people are already sick – to proactive, allowing governments to plan ahead and act before cases spike.

Meet DIRE, a digital, predictive data analysis and visualisation platform for imminent disease epidemics. This tool was funded by Wellcome Trust and developed by the University of California San Diego School of Global Policy and Strategy and New Light Technologies.

DIRE translates disease forecasting into actionable guidance for decision-makers through an interactive map that uses geospatial predictive analytics, showing where dengue and malaria outbreaks are likely to occur and what public resources may be needed to control them.

At the heart of DIRE lies a climate-based ensemble model developed by ESA Φ-lab for UNICEF that uses multiple machine learning approaches and Earth observation products to take account of geographical variations in dengue incidence. The model proved to be more accurate than previous predictive techniques when piloted in Brazil and Peru. This novel approach was selected as one of UNICEF’s top research initiatives of 2022 and one of UNESCO’s Top 100 AI solutions for Sustainable Development Goals.

As the senior author of the study behind Φ-lab’s machine learning approach used in DIRE, Rochelle Schneider (Copernicus Ecosystem Operations Engineer and ESA Φ-lab ambassador) shares her thoughts: “Predicting outbreaks is a challenging work where the complexity is present in data, model, and decision-support layers. By leveraging the machine learning framework we originally developed at Φ-lab, DIRE abstracts these complexities to non-expert users.”

“Seeing this technology transition from the lab to a tool that predicts the needs and resource allocation in Brazil and Peru is the ultimate evidence of Φ-lab’s impact. It aligns with our ‘AI for Good’ mission on creating and implementing new ideas through AI and Earth observation”, Rochelle added.

DIRE focuses on Brazil and Peru, as these two countries have faced persistent, climate-related outbreaks of both dengue and malaria. Its interactive and user-friendly format allow users to view predicted disease risks at multiple geographic levels and see both recent trends and short-term predictions.

The DIRE visualisation platform shows the dengue outbreak risk prediction in Brazilian municipalities (middle). Municipalities with a lower outbreak risk are shown in blue, while the regions with higher outbreak risk are shown in red (as per the map legend on the left-side panel). Municipalities in stripes have a low-confidence prediction. The numbers of young (purple), adult (pink) and total (green) cases per month in a given municipality are shown in the right-side panel. In this panel, the number and cost of commodities/personnel required to mitigate the outbreak and the model indicators used in the prediction are also shown in two separate tabs. Credits: New Light Technologies, Inc.

Users can select a country (Brazil or Peru) to view past reported cases and projections for the current month and up to two months in advance. DIRE provides a range of socio-economic and environmental indicators that were used by the model and flags regions where predictions are less certain, helping users weigh the risks alongside uncertainty.

“UNICEF and ESA previously pioneered machine learning-based predictive models for dengue outbreaks in Latin America by synthesising UNICEF’s granular field data with ESA Φ-lab’s robust Earth observation and machine learning capabilities. This foundational work garnered significant interest from major entities, including the Wellcome Trust, and ultimately served as the analytical backbone for the DIRE project—a private-public collaboration focused on scalability”, commented Do-Hyung Kim, Data Science Specialist at UNICEF’s Climate and Environment Data Unit.

“It is a compelling testament to our partnership that such research initiatives produce high-quality, open-source algorithms that can be scaled to support diverse regions globally. I hope UNICEF and ESA continue to lead in this space”, Do-Hyung added.

DIRE has come a long way in predicting disease outbreaks and its capabilities go beyond forecasting. This platform also estimates the quantity and the cost of commodities and personnel required for disease control and treatment in each region – for example, the number of vaccines and fumigation kits needed, as well as their costs. With these data, DIRE generates a PDF report to be shared with local authorities who need clear information about the risk and resource readiness.

For Carlos Zegarra Zamalloa, Health Specialist at UNICEF Peru, DIRE is a reflection of the collaborative spirit between all stakeholders involved: “Climate-related outbreaks like dengue and malaria are becoming more frequent and dangerous in Peru, especially for children and pregnant women. In 2025 alone, Peru reported 39,000 dengue cases, with a substantial proportion affected being children; the scale has been overwhelming the current capacity of governments and communities to respond effectively. We were therefore delighted to work together with UC San Diego and New Light Technologies to bring a range of stakeholders together to troubleshoot the problem.”

During this soft launch phase, DIRE’s interface and data quality are undergoing improvement tests. The long-term impact of this platform will be determined by its adoption by local authorities to plan and respond to disease outbreaks, supported by real examples and testimonials of its use in the field.

The DIRE visualisation platform is available here. The technical details about the model are available in this Nature Scientific Report’s article.   

To know more: DIRE, ESA Φ-lab, UNICEF, UNESCO, Wellcome Trust, University of California San Diego School of Global Policy and Strategy, New Light Technologies.

Photo courtesy of Unsplash/John Cameron

A thunderous shift in foundation model architecture with THOR

Posted on by mavelar

Foundation models are enabling new ways to use Earth observation data, but most existing models struggle to handle data from diverse sensors and are limited to fixed patch sizes. This makes them hard to use in real-world applications that require flexibility. Funded by ESA Φ-lab and developed by the Norwegian Computing Centre, THOR is a new foundation model designed to overcome both the challenges of heterogeneous inputs and rigid deployment constraints.

Foundation models are driving a paradigm shift in Earth observation, moving the field away from specialised models towards general-purpose geospatial intelligence. Although they promise to revolutionise the way we interact with satellite data, most current foundation models are architecturally rigid.

This means they are trained using a fixed input image size and a fixed patch size (the size of small, non-overlapping segments into which input images are divided before being fed to the model), making it more difficult to process data that differs, even slightly, from the format they saw during training.

Their rigidity creates a bottleneck for data-efficient adaptation: when the workflow breaks down the data into smaller patches, it produces a low-resolution sequence of tokens – units of data that foundation models process to understand the input they were given and then generate an output. Subsequent, dense pixel-level tasks like segmentation will then require large, complex decoders to upsample features. These decoders often require large amounts of data for fine-tuning, undermining the efficiency of foundation models.

Inspired by the Norse god of thunder and his legendary hammer, THOR (Transformer-based foundation model for Heterogeneous Observation and Resolution) is a versatile multi-modal foundation model that will shatter these shortcomings. This model has been developed by the Norwegian Computing Center, funded and supported by ESA Φ-lab through ESA’s Foundation Models for Climate and Society (FM4CS) project.  

THOR is the first foundation model with an architecture that unifies the 10 – 1000m ground sampling distance range of Sentinel-1, -2 and -3, including the OLCI (Ocean and Land Colour Instrument) and SLSTR (Sea and Land Surface Temperature Radiometer) sensors.

This model has been trained on the LUMI high-performance computer using the THOR Pretrain dataset, a 22TB-dataset that has been aligned spatio-temporally and across modalities, and that contains diverse land cover products, digital elevation models, and ERA5-Land variables. By incorporating a randomised patch size and input image size during pre-training, THOR becomes ‘computer-adaptive’.

Other state-of-the-art models like TerraMind, DOFA or Copernicus-FM are flexible in handling diverse inputs, but not so versatile when it comes to deployment. These models have a fixed internal resolution, meaning that, for very fine‑grained tasks like detailed floods or crop boundaries, they often rely on large, complex task‑specific decoders to recover detail.

Instead of locking the model into a fixed image size and resolution, THOR can change its internal resolution at inference time, allowing users to trade accuracy for computational cost without retraining the model: coarser patches could be used for faster, global analyses, while smaller patches can be used for more detailed, local maps.

This way, THOR solves both input heterogeneity and deployment versatility, focusing on making a single model adaptable and efficient across resolutions, data availability, and deployment constraints. THOR achieved state-of-the-art performance and demonstrated its superior data efficiency in the PANGAEA 10% benchmark, a standardised, open-source benchmarking framework designed specifically to evaluate the performance of geospatial foundation models (GFMs). The 10% benchmark refers to a specific, low-data evaluation scenario within PANGAEA designed to assess the effectiveness of GFMs when they are trained using only 10% of the labelled data for downstream tasks. 

Valerio Marsocci, Internal Research Fellow at ESA Φ-lab, comments the importance of THOR for real-world scenarios: “With dense, high‑quality features produced directly from the encoder, THOR often requires much simpler downstream models, which improves robustness and reduces costs. By providing a flexible pre-training starting point, we empower scientists to solve both local and global problems – whether it is mapping disasters or monitoring crop health – without needing to reinvent the architectural wheel.”

For Arnt-Børre Salberg, Chief Research Scientist at the Norwegian Computing Center, THOR sets a new standard for foundation models in the European space ecosystem: “We developed THOR to be a global ‘go-to’ foundation model for Earth observation. This open-access tool transforms satellite data into vital intelligence for maritime security, hydropower energy management and emergency preparedness against floods and avalanches, being an essential tool for a safer, more sustainable future driven by Norwegian innovation.”

THOR is helping Norway consolidate its strategic position in the Arctic region, according to Dag Anders Moldestad, Lead, Earth Observation at the Norwegian Space Agency: “Norway occupies a unique vantage point in the Northern Hemisphere. For us, satellites are not just tools, but our eyes on the ground.”

“What makes THOR a game-changer is its flexibility. It allows us to develop and deploy services in real time with significantly less computing power, so we can respond to crises as they happen. In disaster management, where every second counts, or in tracking the rapid shifts of our climate, THOR provides the speed and efficiency necessary to turn raw data into valuable information”, Moldestad added. 

Find more information about THOR’s technical details in this arXiv paper. The model and pretrain dataset are now available on Hugging Face. Its source code and TerraTorch extension are available on GitHub. A showcase of THOR can be found here.

To know more: FM4CS, ESA Φ-lab, Norwegian Computing Center

Photo courtesy of Unsplash/Mark Kӧnig

A new training explored AI in Earth observation

Posted on by mavelar

From 8 to 11 December, ESA Academy’s Training and Learning Facility in Belgium hosted the pilot edition of the Disruptive Innovation and Commercialisation in Earth Observation Training Course. Organised in collaboration with ESA Φ-lab, this first edition brought together 30 Master’s and PhD students from 16 different nationalities, creating a vibrant and diverse learning environment.

One of the aspects that made this course unique was its dual focus. Participants were trained not only in Artificial Intelligence (AI) applied to Earth observation, but also in the business and commercialisation strategies necessary to turn innovative ideas into viable ventures. This combination of technical and entrepreneurial skills was designed to push students beyond traditional academic boundaries.

“The unique combination of AI, business and Earth observation made it truly one of a kind,” said one student. “Collaborating with motivated participants and learning from the ESA Academy and Φ-lab experts pushed me to think beyond disciplines.”

Read the full article on www.esa.int.

Breaking the satellite trade-off: AI creates near real-time 3D cloud maps

Posted on by mavelar

Clouds play a critical role in Earth’s climate system and are a major source of uncertainty in climate projections. The vertical distribution of ice and water particles in clouds impacts their radiative properties and with that Earth’s energy balance. Recent research also showed that the internal properties of clouds in tropical cyclones influence how storms intensify. Yet satellites face a fundamental trade-off: systems that measure vertical structure lack continuous coverage, while those with continuous coverage cannot see inside clouds.

Now, research conducted through the Earth Systems Lab research programme, funded through the ESA Φ-lab and involving former ESA research fellow, Dr Anna Jungbluth, has developed a breakthrough machine learning framework that translates two-dimensional geostationary satellite imagery into detailed three-dimensional cloud maps in near real-time. Published in November 2025, the study demonstrates for the first time the ability to create global instantaneous 3D cloud reconstructions, with particular success in mapping the internal structure of intense tropical cyclones.

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

Photo courtesy of Unsplash/Zbynek Burival

New ESA-GEOSAT deal to empower space solutions

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ESA and GEOSAT have formalised their commitment to strengthening the space industry by signing a Letter of Intent, promoting entrepreneurship and advancing the development of innovative space solutions.

The latest company joining the portfolio of ESA’s Partnership Initiative for Commercialisation (EPIC) is the Portuguese company GEOSAT, one of Europe’s leading providers in Earth Observation (EO) satellite imagery and data analytics. GEOSAT provides very-high resolution (VHR) optical satellite data in Europe, being certified as a DPS Category 2 Provider (European Earth Observation Established Data Suppliers) and developing innovative EO products and services.

This partnership with GEOSAT will benefit companies supported by ESA Φ-lab, the InCubed Earth Observation commercialisation programme, ESA Phi-LabNET, ESA Business Incubation Centres (ESA BICs) and ESA Technology Brokers, operating under the broader ESA EPIC framework.

As an outcome of this Letter of Intent, GEOSAT will provide VHR data to ESA-supported start-ups, so they can test, validate and improve their services, along with technical mentorship and expertise, and joint outreach and networking activities to foster new opportunities and raise awareness about the societal value of Earth observation technologies. Further information on the collaboration and how to take advantage of the services provided by GEOSAT is now available here.

“By working with GEOSAT and opening access to their Earth observation constellation, we are allowing an easier access to data in order to improve solutions, in areas such as environmental monitoring, agriculture, infrastructure management, and maritime surveillance,” commented Michele Castorina, Head of the ESA Φ-lab Invest Office. “This collaboration not only benefits entrepreneurs and businesses across several sectors but also reinforces Europe’s competitiveness in the global space economy.”

To know more: GEOSAT, ESA Φ-lab, InCubed, ESA Phi-LabNET, ESA Business Incubation Centres, ESA Technology Brokers, EPIC

Photo courtesy of ESA

Earth observation indicators as the key to unlock the Global Goal on Adaptation framework

Posted on by mavelar

The 30th Conference of the Parties (COP30), held in Belém (Pará, Brazil), is strategically focused on implementation, with the challenge of creating a definitive, measurable framework for the Global Goal on Adaptation (GGA). Directly addressing this issue, a new ESA Φ-lab co-led article published in Nature offers a timely intervention, showing how satellite-based Earth observation data can provide the objective, globally consistent indicators needed to achieve climate resilience. 

10 November marked the start of the 30th Conference of the Parties (COP30) in Belém, Pará, Brazil’s gateway to the Amazon rainforest. Regarded as a key point in the global climate agenda, COP30 is shifting the focus from ambition to implementation and accountability, to make the Global Goal on Adaptation (GGA) framework finally operational.

Established by the 2015 Paris Agreement, GGA seeks to improve the ability to cope with climate impacts, build systems that can withstand shocks, and reduce susceptibility to climate hazards. With negotiations culminating on a final set of indicators to measure the progress towards this goal, the success of the summit will lie on implementing a scientifically robust and actionable framework.

Directly addressing GGA – and with a perfect timing – a new ESA Φ-lab co-led article, “Earth observations for climate adaptation: tracking progress towards the Global Goal on Adaptation through satellite-derived indicators”, has just been published in Nature.  This article is the result of the ‘Using Earth Observation Systems to Improve Climate Adaptation Policy and Action’ forum held last year in Bern (Switzerland), hosted by the International Space Science Institute (ISSI).

As part of the work performed within ESA’s Climate Change Initiative (CCI), “the paper highlights Earth Observation’s strengths in providing objective, repeatable, and globally consistent data, while also acknowledging challenges related to data disaggregation, integration with socio-economic factors, and the need for long-term, robust baselines”, the authors stated.

This research details how Earth observation (EO) data are relevant across the entire adaptation cycle, from initial risk to long-term monitoring and evaluation, focusing on four key sectors covered by the GGA framework.

For agriculture, satellites can monitor water-based variables such as evapotranspiration and soil moisture, as well as the status of surface water storages, the evolution of agricultural pests caused by climate change, and shifts in agro-climatic indices like aridity.

Regarding ecosystems and biodiversity, EO is uniquely positioned to measure the extent and changes of ecosystems like coastal mangroves, which serve as natural defences against sea-level rise and storm surges. Adaptation actions like those targeting the climate change-exacerbated threats of pests, droughts, and wildfires, are essential to protect the diverse environmental and socio-economic functions of forests, which include the provision of raw materials for bioeconomy, serving as a wildlife habitat, prevention of soil erosion, and facilitating carbon sequestration.

Extreme events such as floods, droughts, heat waves, or hurricanes, can be monitored by EO technologies, which provide insights across the different stages of the disaster risk management cycle, from pre-event assessment to post-event recovery. Additionally, EO data can be used to quantify vulnerability through detailed mapping of inhabited areas, building footprints, roads, and critical infrastructure such as dams.

EO data are instrumental in monitoring health-related hazards, in particular heat extremes, infectious diseases, and air pollution from wildfires, being frequently used as the input for models to produce hazard or exposure maps. While satellite-based data do not directly capture health outcomes, they are a proxy to perform health assessments at different levels: individual, household, cohort, or administrative.

Despite its immense potential, the authors stress that EO is not a solution on its own and provide recommendations for both the policy and scientific communities. They strongly urge negotiators to integrate EO data into the final GGA indicator toolbox that is set to be adopted at COP30.

Simultaneously, the authors call on the EO community to focus on how to operationalise EO-based adaptation data and information to make them easily accessible to policy makers. They also recommend a substantial investment in end-user training and embedding geospatial data science experts within operational agencies, especially in vulnerable regions, as this is essential for effectively using EO data for adaptation solutions.

The article concludes by highlighting that EO data must be integrated with socio-economic and local data to ensure it accurately reflects the context of adaptation and does not overlook the vulnerability of specific, often marginalised populations. 

Rochelle Schneider, Copernicus and Destination Earth Ecosystem Operations Engineer and the second author of the paper, commented: “To track real progress on adaptation, we need data that peacefully crosses borders effortlessly. This is exactly what EO satellites provide — globally harmonised evidence to support collective action against climate change impacts.”

This Nature perspective serves as the foundation for other efforts underway at ESA Φ-lab: Diego Jatobá dos Santos, an International Research Fellow supervised by Rochelle Schneider, is working on a project to assess the climate risks faced by children under different climate zones and climate change scenarios in Brazil, in a fruitful collaboration with UNICEF.

Diego will investigate an adjustment in UNICEF’s Children’s Climate Risk Index (CCRI) for Brazil climate zones, using diverse geospatial datasets and predicting CCRI under different climate change scenarios, with CMIP6 and/or Destination Earth Digital Twin data.

Building on this effort, Φ-lab is currently recruiting a Research Fellow to work on Artificial Intelligence (AI) for Climate Adaptation. The new lab member will investigate how AI can play a significant role in climate adaptation, resilience and mitigation.

The full Nature article is available here.

To know more: COP30, ESA Φ-lab, UNICEF’s Children’s Climate Risk Index (CCRI)

The banner image features forests around the Capim River (Rio Capim) in Brazil. Contains modified Copernicus Sentinel data (2022), processed by ESA.

Less is more: the power of TerraMind in your pocket and in space

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On the last Earth Day, the European Space Agency (ESA) and IBM Research Europe launched TerraMind, a multimodal Earth observation foundation model. While powerful, the model’s need for large computing capabilities for fine-tuning and inference poses certain challenges, particularly in remote or resource-constrained environments. In October 2025, two lighter versions of TerraMind were released to be run on edge devices – like laptops or directly onboard satellites – enabling on-device, real-time analysis. The TerraMind Blue-Sky Challenge launched to even further improve TerraMind and has two new deadlines: 30 November 2025 and 31 January 2026.

Geospatial and Earth observation foundation models are crucial for overcoming global challenges. Pre-trained on vast, globally diverse datasets using self-supervised learning, these models learn a universal “language of Earth”, meaning that they can be quickly fine-tuned with minimal new data and supervised learning to perform a wide range of complex tasks, from disaster response and damage assessment to crop yield forecasting and wildfire spread forecasting.

This drastically reduces the cost, time, and data-labelling efforts required to create actionable insights, making complex Earth observation data more accessible for scientists, policymakers and communities worldwide.

As recalled during the last ESA-NASA International Workshop on AI Foundation Models for EO, the broader challenge now lies in moving beyond rapid-cycle prototyping toward the operational deployment of foundation models for real-world decision-making and societal benefits.

Achieving this requires designing models with deployment as a central consideration — ensuring they are efficient, accountable, and seamlessly interoperable with downstream systems such as digital twins, public dashboards, and early warning platforms. Enabling edge deployment, including onboard satellites, is vital for real-time analysis in resource-constrained environments.

To support this transition, building smaller but efficient models together with robust and scalable software stacks is essential to empower the broader community to adopt and apply these models effectively and responsibly.

In April 2025, ESA Φ-lab and IBM Research Europe launched TerraMind, a multimodal Earth observation foundation model. TerraMind is currently the best performing Earth observation foundation model, assessed by open community benchmarks such as PANGAEA, as it is the only model to outperform other traditional Earth observation methods with a frozen encoder strategy.

Six months later, two lighter versions of TerraMind – TerraMind.tiny and TerraMind.small – were released, specifically engineered to operate on normal or edge hardware devices. Despite their reduced size, these lightweight versions of TerraMind have little decrease in performance – compared with its full-scale version – allowing for model fine-tuning to any downstream use cases just with a standard computer, even without GPUs, or deploying the model to the ultimate edge, directly onboard orbiting satellites.

Performance of various TerraMind models (in blue) compared to other established foundation models (e.g., Prithvi 1.0, Prithvi-v2-300M), as assessed by the PANGAEA benchmark. mIoU (mean Intersection over Union) is a measure for performance in segmentation tasks. The higher mIoU value, the better a model performs.

Besides bringing Earth observation capabilities to edge devices, the creation of the ‘tiny’ and ‘small’ versions of TerraMind also lowers the entry barrier for Earth observation researchers, field workers, or smaller organisations. By drastically reducing the hardware requirements, anyone with a laptop can fine-tune these models and create new applications to better monitor Earth.

“The open-sourcing of the ‘tiny’ and ‘small’ TerraMind models is a game-changer for the entire Earth observation community. This is a clear step towards the true democratisation of planetary-scale foundation models,” commented Nicolas Longépé, Earth Observation Data Scientist at ESA Φ-lab.

“These lightweight versions shatter a barrier imposed by the lack of access to the latest, most powerful GPUs. They maintain industry-leading performance while running efficiently on edge devices and, most importantly, they move the power of cutting-edge AI from the exclusive domain of research centres and companies to the hands of the many who need it most,” Nicolas added.

Although the ‘tiny’ and ‘small’ versions of TerraMind represent a major leap in accessibility, innovation does not stop there: the TerraMind Blue-Sky Challenge, organised by ESA Φ-lab and IBM Research Europe, welcomes innovative ways to push TerraMind beyond “just another fine-tune”, whether it is prototyping a new multimodal workflow, exploring Thinking-in-Modalities, pushing the limits of these tiny but mighty models, or inventing a never-seen geospatial application.

This bi-monthly award has two new submission deadlines – 30 November 2025 and 31 January 2026, 23:59 (AoE). Each winner will receive a € 1.000 cash prize, with a potential publication in a joint wrap-up paper and broad visibility across IBM, ESA, and the Earth observation community. More information about this challenge can be found here.

All versions of TerraMind can be found here. TerraMind was developed within FAST-EO, an initiative led by a consortium comprising DLR, Forschungszentrum Jülich, IBM Research Europe and KP Labs, and supported and funded by ESA Φ-lab.

To know more: ESA and IBM collaborate on TerraMind, IBM ESA Geospatial @ Hugging Face, TerraMind Blue-Sky Challenge

Photo courtesy of Unsplash/Conny Schneider

Four new initiatives to boost Spain’s Earth observation sector

Posted on by mavelar

As the result of an ESA-dedicated commercialisation campaign for Spain, the InCubed Programme signed four new contracts with IVSEN, HAPSEYE, CrossBandInsights, and DVSTAI. From energy infrastructure monitoring to security and geospatial object detection, these projects reflect the growing impact of Earth observation data across key sectors.

Following the success of the last dedicated call for Spain, four initiatives signed a contract with the ESA InCubed programme. This call, launched in collaboration with the Spanish Space Agency (AEE), offered different levels of co-funding to develop innovative and commercially viable Earth observation products and services, while benefitting from the European Space Agency’s technical, commercial and financial guidance.

IVSEN is an advanced satellite-based monitoring solution tailored for energy infrastructure operators. It integrates a very-high-resolution payload (< 50 cm) with reduced mass and volume, along with agile observation modes for flexible operations. An on-board pre-processing algorithm works in tandem with the ground-based processing chain to generate specialised data products and analytics. This project is being developed by a consortium – SATLANTIS, Alén Space, DHV Technology, and GeoAI – with direct contributions from users such as Iberdrola to ensure the system meets real operational needs.

“IVSEN represents a strategic milestone for SATLANTIS, as it strengthens our capabilities in very high-resolution Earth observation — a core technology for the company’s future. We are grateful to ESA for their trust and support in driving this project forward, and for enabling us to deliver an agile solution that will help energy operators and other users monitor and safeguard their critical infrastructures,” stated Juan Tomas Hernani, CEO of SATLANTIS.

ICEYE delivers synthetic aperture radar (SAR) data worldwide through its fleet of satellites, supporting applications such as land use monitoring, border surveillance and environmental monitoring. To expand this capability, the company is developing HAPSEYE, a solar-powered, fixed-wing aircraft designed to operate at altitudes above 20 km for extended periods.

Equipped with a SAR payload, HAPSEYE will complement ICEYE’s satellite constellation by providing persistent, high-resolution imaging that overcomes current limitations in coverage and resolution. This next-generation platform will improve disaster response, security and environmental monitoring. The activity is planned to begin after the test campaign of HAPS Prototype-1, scheduled for late 2025.

“As a pioneer in SAR imaging radar satellite innovation, we are delighted to have been chosen for ESA’s InCubed programme in Spain. Initiatives like this are crucial for accelerating technological advancement and strengthening European competitiveness in the Earth Observation sector – objectives that resonate strongly with our mission at ICEYE. This commitment is underlined by the high-altitude platform station project we are taking on as part of the programme, designed to aid European natural disaster response and Earth Observation capabilities,” stated Lauri Väin, VP of High-Altitude Platforms at ICEYE.

TRE ALTAMIRA delivers satellite radar (SAR) displacement measurements and mapping solutions for sectors such as civil engineering, mining, oil, and gas. Its product, CrossBandInsights, enhances current single-frequency band interferometric SAR (InSAR) products, by combining X- and C-band observations with higher spatial and temporal observations to improve ground movement monitoring. This allows for engineering firms and authorities to detect subtle changes, supporting smarter infrastructure maintenance decisions and strengthening risk management with enhanced spatial and temporal coverage.

“InCubed Spain has given us the unique opportunity to turn our vision into a concrete product that will bring tangible benefits to the Earth observation market. CrossBandInsights addresses a critical need by merging multi-mission C-band and X-band InSAR data to provide more accurate and timely insights on ground deformation,” commented Roberto Montalti, Project Manager at TRE ALTAMIRA.  

“This innovation will support civil engineering companies and public authorities in ensuring infrastructure safety and resilience. We see this project as a clear example of how public funding can be effectively invested to foster innovation, create market-ready solutions, and strengthen Europe’s position in the space sector,” Roberto added.

Thales Alenia Space, a global leader in space manufacturing, has been delivering advanced solutions in telecommunications, navigation, Earth observation, environmental management, science and orbital infrastructures for over 40 years. Among its innovations is DVSTAI (Deeper Vision Self-Trained AI), an evolution of the SatHound project, designed to overcome the limitations of current geospatial object detection methods: traditional approaches often require expert intervention for model design, training, and deployment, making the process slow, costly, and vulnerable to risks such as unauthorised access or data leakage.

DVSTAI addresses these challenges by leveraging deep learning techniques, allowing even non-AI or non-Earth observation specialists to autonomously train and use models through a user-friendly software solution. These models can be tailored to specific applications, including object detection, change detection, and semantic segmentation.

“DVSTAI is a user-centric AI solution that empowers non-technical users to autonomously create, train, and deploy AI models for object detection and vision tasks over satellite imagery. It simplifies the process, reduces costs, and enhances security by eliminating the need for dedicated AI engineers to develop high performing vision models, making it an invaluable tool for EO analysts and service providers,” commented Julian Cobos, Product Line Manager at Thales Alenia Space Spain.

“Thanks to the ESA InCubed programme, Thales Alenia Space will develop new key capabilities for object detection in Very High Resolution (VHR) and Synthetic Aperture Radar (SAR) data and bring DVSTAI solution to the public Cloud in a Software as a Service (SaaS) model, making it accessible for any user to set-up object detection campaigns over open and commercial data sources,” Julian added.

To know more: ESA InCubed, Spanish Space Agency (AEE)

Photo courtesy of Unsplash/Chris Boland