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Category: Philab

SHRUG-FM wins ‘Best Paper Award’ at the EarthVision 2026 workshop

Posted on by mavelar

Building on the success of the 2025 FDL Earth Systems Lab (ESL)’s research sprint, SHRUG-FM, a framework for reliability-aware prediction that enables geospatial foundation models to identify and abstain from likely failures, won ‘Best Paper Award’ at the EarthVision 2026 workshop that took place during the Computer Vision and Pattern Recognition (CVPR) 2026 Conference.

Geospatial foundation models are very useful tools for Earth observation research since they allow a single, pre-trained AI model to be quickly adapted for multiple tasks, using drastically less labelled data than more conventional AI models. However, these models can be ‘confidently wrong’, failing to perform reliably in environments that were underrepresented during their training phase.

This becomes particularly problematic when these geospatial foundation models are used in critical, time-sensitive situations such as disaster response after an extreme weather or climate event. But how is it possible to increase model transparency in cases where the model output has a high degree of uncertainty and requires human validation? A team mentored by Ruben Cartuyvels, Internal Research Fellow at ESA Φ-lab, and Patrick Ebel, former Internal Research Fellow at ESA Φ-lab and researcher at Google Research, decided to dive deep into new ways of making foundation models aware of their own uncertainty.

During the 2025 FDL Earth Systems Lab (ESL)’s ‘Foundation Models for Extreme Environments’ sprint, the team developed SHRUG-FM (Systematic Handling of Real-world Uncertainty for Geospatial Foundation Models), a framework that helps foundation models flag when they may fail, addressing two main types of uncertainty: data-driven or model-driven.

SHRUG-FM was developed as mechanism that can either provide a prediction, raise a warning or ‘shrug’ – indicating it doesn’t know the correct answer – when a foundation model is highly uncertain. This adaptable framework aims to make geospatial foundation models more transparent and reliable, showing a consistently reduced prediction risk for critical, climate-sensitive tasks like burn scar segmentation, flood mapping and landslide detection.

On 4 June 2026, SHRUG-FM was presented during the EarthVision 2026 workshop. As part of the Computer Vision and Pattern Recognition (CVPR) 2026 Conference, this workshop aimed to strengthen collaboration between the Earth observation, computer vision and machine learning communities, fostering innovation in automated geospatial analysis. SHRUG-FM won ‘Best Paper Award’, and its paper and code are now available online.

Vasileios Sitokonstantinou, member of the SHRUG-FM team, holds the ‘Best Paper Award’ certificate. From left to right: Ronny Hänsch, Vasileios Sitokonstantinou, Nathan Jacobs and Hannah Kerner.

Claudio Iacopino, Head of the ESA Φ-lab Explore Office, commented on the importance of developing frameworks like SHRUG-FM: “Winning ‘Best Paper Award’ proves that the Earth observation research community is shifting its focus to a crucial point in the use of geospatial foundation models: trust. For a long time, the goal was to make these models smarter, and even though that is still an objective, it’s very important to have frameworks that give these models the ability to say ‘I might be wrong here’. Building this kind of reliability is exactly what both research and industries need to see before they fully adopt these models for widespread, everyday use.”

A more detailed explanation about the development of SHRUG-FM is available here. 

To know more: ESA Φ-lab, FDL Earth Systems Lab (ESL), EarthVision 2026, Computer Vision and Pattern Recognition (CVPR) 2026 Conference

Photo courtesy of ESA, contains elements from FDL Earth Systems Lab.

Grab your seat for Φnnovation Summit

Posted on by mavelar

From 23 to 25 June, ESA Φ-lab will host Φnnovation Summit at ESA-ESRIN in Frascati (Italy). This event will explore how emerging, disruptive technologies can transform the future of Earth observation.

The first edition of Φnnovation Summit will take place from 23 to 25 June 2026 at ESA-ESRIN. Organised by ESA Φ-lab, Europe’s Earth observation disruptive innovation hub, the event will bring together forward-thinking researchers, bold entrepreneurs and technology pioneers to explore the future of Earth observation.

The event was curated to offer something valuable to every attendee: from exploring transformative innovations like Artificial Intelligence, Machine Learning and Quantum Computing to Neuromorphic and Edge Computing, Cognitive Space and Immersive Visualisation, this gathering will spotlight disruptive technologies and approaches that could redefine the future of Earth observation.

Φnnovation Summit stands out from other traditional conferences by emphasising the interaction between participants and open exchange. Its community-driven programme features multiple parallel sessions, from fast-paced PechaKucha talks to Fishbowl dialogues, encouraging participants to actively shape the agenda and contribute with their perspectives.

Every morning of the event kicks off with a high-impact main plenary, setting the stage with compelling panel discussions and keynote speeches. Attendees will get insights from directors of ESA, ECMWF, EUMETSAT and the Kenya Space Agency, as well as experts from NASA JPL, ASI, CNES, DLR, the European Commission, Vector Institute, SatCen, ETH Zürich, CERN, Jülich Supercomputing Centre, University of Oxford, Stanford University and MIT, and leading industries like Enel, Pasqal, IonQ, Leonardo, OHB, Thales, Planet Labs and Mistral AI, among many others. 

On 23 and 24 June, talented BSc, MSc and PhD students will take the stage to deliver five-minute pitches, showcasing their bold ideas and research in Earth observation and emerging technologies. Each day will conclude with the selection of a Pitch Hour winner, who will be offered the opportunity to join ESA Φ-lab as a Visiting Researcher.

During Innovation Speed Dating, researchers, entrepreneurs and innovators will have the opportunity to connect through a series of quick, focused one-on-one conversations with ESA staff members.

On 23 June, a relaxed ESA-hosted Cocktail and Buffet Dinner will offer the attendees a moment to unwind, connect with fellow participants and spark new conversations over a refreshing drink. On the night of 24 June, the Gala Dinner will offer a unique opportunity to connect with leaders, innovators and peers, in an elegant evening of fine dining and high-level exchange.

To keep the conversation going, attendees can join one of our non-hosted Thematic Dinners on 25 June. Small groups will be gathered around three curated topics: ‘Technology Push’, ‘Innovation Impact’, and ‘Foundation Models, GenAI and Quantum Computing’. This will be an opportunity to dive deeper into shared interests, meet new people and enjoy good food.

On 26 Jun, participants will have the opportunity to join an unforgettable cultural trip to Rome. Details will be provided soon.

More information about the event and registrations is available on the Φnnovation Summit website.

To know more: ESA Φ-lab

Photo courtesy of ESA.

Discover detailed thermal data from constellr

Posted on by mavelar

As part of an announcement of opportunity, SkyBee data products are being disseminated to researchers for use in scientific and pre-operational development studies, following the approval of a Project Proposal by ESA and constellr. Thermal insights from constellr’s SkyBee satellites are set to support innovative research into urban heat mapping, drought assessment, environmental monitoring, and much more.

The current announcement of opportunity forms part of an extended and successful collaboration between ESA and constellr. In 2022, the company received backing from the ESA InCubed programme, which supports companies through co-funding, as well as technical, industrial, and business development mentoring. Shortly after, the company was announced as an emerging Copernicus Contributing Mission in 2023 and secured further backing from a number of investors.

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

Photo courtesy of constellr.

SPRINT4EO: giving Europe a running start in Earth observation innovation

Posted on by mavelar

As the demand for Earth observation capabilities increases by the day, innovation must keep pace. Funded by ESA Φ-lab and implemented by OHB Digital Services, SPRINT4EO is a new initiative to create a rapid prototyping environment where small, focused teams work on breakthrough ideas, benefitting from technical expertise outside the traditional space sector.

What if we could repurpose disruptive technologies from other domains and introduce them into the Earth observation ecosystem?

To maintain its relevance and ensure Earth observation remains a pillar of the global digital economy, we must bring fresh perspectives. By adopting existing tools rather than building tailored Earth observation solutions from scratch, a cross-disciplinary approach can turn Earth observation into a high-speed technological ecosystem capable of scaling at the pace of modern data demands.

This is precisely what SPRINT4EO aims to do. As part of the ESA Φ-lab initiative ‘EO Foresight Exploratory Sprints’ and led by OHB Digital Services as the prime contractor and coordinator, “SPRINT4EO is designed to shorten the path from promising ideas to concrete Earth observation applications,” comments Patrick Rückert-Schindler, Proposal and Project Manager at OHB Digital Services.

“By running focused research sprints with specialised external teams, the initiative creates room to test disruptive technologies quickly and in an application-driven way”, Patrick added. SPRINT4EO enables a rapid prototyping framework within ESA to encourage the participation of companies that never had the opportunity to work with the agency.

A helping hand from medical technology

One of the implementers of SPRINT4EO is the Fraunhofer Institute for Digital Medicine MEVIS, which combines applied research with robust software development, targeting real-world use cases in healthcare. MEVIS’ focus has been on integrating advanced image and data analysis into tools that support diagnosis, therapy or clinical decision-making.

The resulting expertise with AI-driven analysis and complex imaging workflows is what Fraunhofer MEVIS now brings into Earth observation through SPRINT4EO: Earth Observation Multi Agent System (EOMAS) is a sprint focused on developing an agentic AI assistant prototype for Earth observation queries that will understand a user’s query, plan the required workflow, and use dedicated tools for data access, image processing and visualisation.

“It is very exciting for us to apply our expertise to the Earth observation domain. There are interesting analogies between histopathology and satellite imaging, despite the vast difference in scale. Within SPRINT4EO, we explore the possibilities of agentic systems together,” commented Hans Meine, Head of Image Analysis and Deep Learning at Fraunhofer Institute for Digital Medicine MEVIS.

From point clouds to carbon storage

Pointly GmbH is a Berlin-based geospatial start-up specialised in cloud-based analysis, management, and classification of large 3D point clouds. Its platform combines pre-trained and custom AI models, manual annotation tools, vectorisation, and scalable cloud workflows to turn raw point cloud data into structured geospatial information for applications such as urban planning, infrastructure monitoring, and digital twin creation.

In SPRINT4EO, Pointly builds on this 3D geodata and AI expertise to lead the Carbonherence sprint, which aims to create a hybrid workflow for dynamic urban biomass for the purpose of carbon assessment. It combines multispectral, radar, thermal, and LiDAR-based information to create a scalable method for estimating vegetation structure, which will then be used to assess carbon storage in cities.

“SPRINT4EO gave us the framework to take an idea we had long been developing, combining Pointly’s 3D AI capabilities with multi-source Earth observation data and turn it into a rigorous, scalable workflow. Carbonherence is exactly the kind of challenge that benefits from ESA’s support and only possible when space data and AI innovation work in concert,” commented Sid Hinrichs, Head of Operations at Pointly GmbH.

Making Sentinel-2 image analysis more powerful

Zentrix Lab is an Estonian small and medium-sized enterprise with a strong profile in research, innovation and software development. The company works across areas such as Artificial Intelligence, the Internet of Things, and Earth Observation, while also delivering commercial solutions for horizontal market sectors.

This company leads the HYPERFUSE sprint, which is advancing a new AI-enabled fusion layer designed to transform Sentinel-2 imagery into harmonised, very-high resolution (VHR) proxy mosaics. By preserving the radiometric quality of Sentinel-2 data while introducing VHR-like structural organisation, the approach aims to support more effective downstream Earth observation analytics, particularly in settings where access to real very-high resolution imagery is limited or too expensive.

“With HYPERFUSE, we explore how AI-driven fusion can enhance the analytical value of Sentinel-2 data by introducing a harmonised high-resolution proxy layer, aiming to support more scalable and cost-efficient Earth observation analytics,” commented Anđela Marković, Researcher at Zentrix Lab.

Cross-disciplinary collaboration is the key for success

Looking beyond the traditional space sector expertise and embracing fresh perspectives is key to generate innovation in the Earth observation domain and to strengthen the European Earth observation industrial competitiveness.

“A key strength of SPRINT4EO is the combination of the Earth observation domain expertise, agile execution and external innovation capacity. That creates a practical framework for exploring new technologies before they move into larger operational contexts,” commented Mounia El Baz, Earth Observation Digital Innovation Engineer at ESA Φ-lab and Technical Officer for SPRINT4EO.

“The first round of research sprints already shows the wealth of innovation the initiative aims to unlock – an agentic AI model for Earth observation question answering, multi-sensor carbon intelligence for cities, and AI-enabled fusion that makes Sentinel data analytically more powerful,” Mounia added.

The SPRINT4EO activities are organised in three overlapping lots, with individual sprints running for up to six months after the kick-off date. The initiative will run until June 2027.

More information about SPRINT4EO and how to participate can be found here.

To know more: SPRINT4EO, ESA Φ-lab, OHB Digital Services, Fraunhofer Institute for Digital Medicine MEVIS, Pointly GmbH, Zentrix Lab

Rome, Italy, is featured in this image captured by the Copernicus Sentinel-2 mission. Photo contains modified Copernicus Sentinel data (2020), processed by ESA.

Turning data from space into action for Earth

Posted on by mavelar

Happy Earth Day, 22 April – a global call to act and protect our planet. At the European Space Agency, that action begins in orbit, where satellites deliver a continuous, global view of Earth and track environmental change.

Working with partners, ESA turns this stream of data into actionable information through its FutureEO programme, helping governments and communities respond faster and more effectively to climate-driven risks.

As climate change accelerates the spread of mosquito-borne diseases, satellite-based early warning systems are giving health authorities a critical head start. By fusing Earth observation data with machine learning, ESA and UNICEF have developed a digital platform that helps countries predict, prepare for and respond to dengue and malaria outbreaks weeks before they escalate.

At the heart of this effort is the Disease Incidence and Resource Estimator (DIRE), developed by ESA Φ‑lab for UNICEF. DIRE combines machine learning with satellite‑derived environmental data to model how climate and geography influence disease transmission and predict imminent disease epidemics.

Read the full article on www.esa.int.

Photo courtesy of Pixabay/A Different Perspective

EVE: making Earth observation knowledge accessible to everyone

Posted on by mavelar

Despite the wealth of Earth observation and Earth sciences knowledge, much of it is scattered across many different sources and formats and only accessible to experts. EVE (Earth Virtual Expert) is a new intelligent companion for exploring the world of Earth observation and Earth sciences that can explain both beginner and advanced concepts, guide users to trusted sources, summarise scientific documents and deliver insights on trends and tools, acting as a centralised platform for Earth observation and Earth sciences insights.

Anyone who has ever had to compile information for a report knows how difficult it is to include all the pertinent data and cross-check references. This is the reality in several domains – and Earth observation and Earth sciences are no exception.

Earth observation and Earth science research generates a lot of high-value knowledge, but this knowledge is scattered across many different sources and formats. Accessing this information usually requires deep expertise, limiting comprehensive understanding.

All of this creates a significant entry barrier for many potential users, like domain practitioners and decision-makers who need transparent, trusted and scientifically robust information – something that traditional systems struggle to provide.

As environmental decisions and interventions rely more and more on Earth observation, there is the need for systems that not only retrieve information but also interpret and reason across heterogeneous sources. Recent advances have been made in the field of large language models (LLMs), but general-purpose models lack the domain specificity and rigorous evaluation needed for reliable Earth Intelligence applications.

Meet EVE (Earth Virtual Expert), an Earth observation and Earth science-specialised LLM. Funded by ESA Φ-lab and built by Pi School, EVE was developed in partnership with Imperative Space and Mistral AI to close the gap between Earth sciences and decision-making.

EVE-Instruct, the core 24B LLM for Earth Intelligence behind EVE’s chat platform, was built on Mistral’s Small 3.2 model and further optimised for reasoning and question answering. As corpus design and domain-adaptive pre-training are central to the performance of a specialised LLM, the team curated a large-scale Earth observation and Earth sciences corpus by manually selecting 172 sources across 22 trusted publishing institutions that included open-access, private and proprietary collections, the latter as the result of a partnership agreement with Wiley.

Adapting an instruction-tuned LLM to a target domain may come at the expense of the ability of the model to follow instructions, its conversational stability or tool-use behaviour. The team implemented a fine-tuning strategy that interleaves instruction fine-tuning and long-form text, mixing general-domain replay data with synthetic Earth observation and Earth sciences text. As a result, EVE-Instruct is more stable and can follow instructions better than its parent model.  

Due to a lack of standardised benchmarks for dialogue and natural language processing capabilities applied to Earth observation and Earth sciences, the team also curated an evaluation set targeting domain-relevant tasks like multiple choice question-answering (MCQA), hallucination detection and open-ended question-answering (QA), in what constitutes the first systematic benchmarks within Earth observation and Earth sciences for language modelling.

EVE-Instruct was evaluated using these benchmarks and general-domain benchmarks to access domain gains and preservation of its general capabilities. It was compared against the parent model and three additional LLMs of comparable scale: Mistral Small 3.2, Gemma3, Qwen3 and Llama4 Scout, respectively.

EVE-Instruct achieved the highest performance across MCQA benchmarks, indicating effective incorporation of Earth observation and Earth science knowledge during the fine-tuning step. It also leads competing models on open-ended QA without context under both the ‘LLM-a-as-judge’ and ‘Win Rate’ evaluations.

To address the issue of factual hallucinations and to extend EVE’s knowledge beyond the training data, the team developed a Retrieval-Augmented Generation pipeline that grounds EVE’s answers in relevant documents from team-curated Earth observation and Earth sciences knowledge bases.

For hallucination detection, EVE-Instruct goes through a first fact-checking stage after a query, in which it acts as an evaluator, producing a binary hallucination label and a justification for the label. If any hallucination occurred, the query is reformulated using the justification to address the identified issues. With newly retrieved information, the model generates a revised, more grounded response.

EVE-Instruct has the ability to critique the original answer using both prior and newly retrieved evidence and then produce a revised answer. In the end, the model ranks the original and revised outputs, selecting the most evidence-supported, reliable response.

Beyond offline evaluation, a six-month pilot stage for EVE’s chat platform was carried out starting in September 2025 with the help of 350 users, through a graphic user interface and an application programming interface. Interested parties can read more about the development of EVE in this technical paper and in this one-pager.

The models, code, curated corpus, benchmarks and a subset of the synthetically-generated fine-tuning dataset used to create EVE-Instruct are now available on EVE-ESA’s Hugging Face and GitHub.

By using EVE’s chat platform, anyone – despite scientific background and level of expertise – can explore and ask Earth observation and Earth science-related questions using natural language. This platform will be able to explain both beginner and advanced concepts, guide users to trusted sources, summarise scientific documents and deliver insights on trends and tools.

An operational version of platform is undergoing its final stages of development and will be available soon. Registrations for its public launch are now open here.

For now, EVE is text-only and does not reason directly over Earth observation and Earth sciences imagery or structured geospatial data. However, the team aims to expand it into a multimodal, agentic platform capable of reasoning over imagery and geospatial data, supporting multi-step scientific workflows for large-scale Earth observation and Earth sciences analyses and data-driven inference.

To facilitate this transition, the next steps have already been prepared: EVE natively operates using the standard Model Context Protocol (MCP), enabling seamless connectivity with a wide range of external geospatial tools, services, and processing backends. This design choice ensures that multimodal and agentic capabilities can be integrated incrementally, allowing EVE to orchestrate and interact with geospatial computation resources as they are plugged into the MCP ecosystem.

Discover more about EVE and register for EVE’s public opening on the EVE website.

To know more: ESA Φ-lab, Pi School, Imperative Space, Mistral AI, Wiley

Photo courtesy of Unsplash/Vimal S.

OroraTech’s journey from start-up to Copernicus data provider

Posted on by mavelar

Since joining ESA’s European Emerging Copernicus Contributing Missions (CCM) activity in June 2023, OroraTech has grown from a space start-up to a leading supplier of thermal sensing data and predictive wildfire solutions.

The Munich-based company now operates a growing constellation of thermal-sensing spacecraft and recently reached a significant milestone – participating in a hands-on workshop directly with the Copernicus Emergency Management Service (CEMS).

OroraTech’s relationship with ESA began through the ESA BIC Bavaria and ESA Kick-Start incubation programmes. This was followed in 2022 by co-funding from ESA’s InCubed programme for the development of FOREST-3 – the company’s first fully internally developed spacecraft, which launched in January 2025.

Read the full article on www.rapidresponse.copernicus.eu.

Photo courtesy of Unsplash/Mike Newbry.

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