August 12, 2025
A bold new chapter for AI4EO with ‘ESA Φ-lab Challenges’
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:
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.
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.
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