When disaster strikes, time is critical. With this challenge, the ESA Φ-lab and the International Charter ‘Space and Major Disasters’ invite you to develop AI models that can rapidly detect earthquake damage using satellite imagery – helping emergency teams assess destruction faster and act where it matters most. Bring space, AI, and impact together to support real-world crisis response. […]
PANGAEA is a new evaluation and benchmarking protocol that covers a diverse set of datasets, tasks, resolutions, sensor modalities, and temporalities. This challenge is open-ended, giving participants the flexibility to explore, experiment, and iterate on their models over time. Alongside the open challenge, we’ll be launching regular Data Sprints – short, high-impact tasks that run for about two months each and focus on specific use cases within the PANGAEA dataset. These sprints will come with their own goals, deadlines, and prizes. […]
The new HYPERVIEW2 Challenge is implemented as part of the Explainable AI in Space (EASi) workshop, that KP Labs, ESA, and the Φ-Lab Challenges team are organising as part of the European Conference on Artificial Intelligence (ECAI), which is hosted in Bologna, Italy from 25th–30th October 2025. […]
Your city holds secrets ready to be revelead. We invite you to be part of this story and join the #MapYourCity challenge. […]
“This exciting challenge aimed to explore the cutting-edge possibilities of in-orbit data processing and its potential to drive progress in earth sustainability, business, industry, and science. […]
An extraordinary challenge has been created to bring together Artificial Intelligence (AI) and Earth Observation (EO) players to address the challenge of “automated sea ice mapping” from Sentinel-1 SAR data. […]
Agriculture plays a central role globally by providing food for humans and livestock or material for industrial processes. It is a driver of economic growth and will play an essential role in reducing the impact of climate change and greening our economies. […]
Maintaining farm sustainability through improving the agricultural management practices by the usage of recent advances in Earth observation and artificial intelligence has become an important issue nowadays. It can not only help farmers face the challenge of producing food at an affordable price, but can also be crucial step toward the planet-friendly agriculture. […]
Remote sensing is entering a new era of time-series analysis. Daily revisit times of satellites allow for near real-time monitoring of many areas across the globe. However, there has been little exploration of deep learning techniques to leverage this new temporal dimension at scale. […]
The ESA Φ-lab Air Quality & Health challenge was a joint effort of the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Copernicus Atmosphere Monitoring Service (CAMS). It focused on developing AI-powered solutions to facilitate the downscaling of analyses and forecasts of surface concentrations of particulate matter (PM2.5) and nitrogen dioxide (NO2). […]
Φ-lab Challenges is dedicated to organising cutting-edge innovative challenges in partnership with esteemed partners and sponsors. These challenges not only promote the growth and engagement of the Φ-lab community, but also provide a platform for researchers and talented innovators to showcase their work and make a tangible impact in solving some of society’s most pressing challenges.
By leveraging transformative technologies like AI, ML, Quantum Computing, and others, and utilising Earth observation, we aim to exctract valuable insights and information to drive positive change for human prosperity.
Φ-lab Challenges is an initiative of ESA Φ-lab implemented by a consortium of private companies and startups composed of Novaspace, Planetek Italia, Sinergise, GMATICS and EarthPulse.
Earthquakes strike suddenly and unpredictably, often causing widespread destruction to infrastructure, buildings, and essential services. Rapid damage assessments can guide emergency responders to the hardest-hit areas, help identify ongoing risks, and support life-saving decisions when time is of essence.
The ESA Φ-lab and the International Charter ‘Space and Major Disasters’ invite data scientists, AI researchers, developers and EO enthusiasts from around the world to join the AI for Earthquake Response Challenge – to build machine learning models capable of rapidly identifying post-earthquake building damage using EO data.
Join now, and make a real-life difference.
New name, new season, new exciting challenges. ESA’s AI4EO is back, well kind of – with a new name and logo ESA Φ-lab Challenges is excited to reveal a new series of new challenges! Stay tuned, more information coming soon.
With HYPERVIEW2, we’re challenging researchers, engineers, and data scientists to explore new ways of making AI interpretable and explainable for satellite data processing, space operations, and Earth monitoring.
When disaster strikes, time is critical. With this challenge, the ESA Φ-lab and the International Charter ‘Space and Major Disasters’ invite you to develop AI models that can rapidly detect earthquake damage using satellite imagery – helping emergency teams assess destruction faster and act where it matters most. Bring space, AI, and impact together to support real-world crisis response. […]
The new HYPERVIEW2 Challenge is implemented as part of the Explainable AI in Space (EASi) workshop, that KP Labs, ESA, and the Φ-Lab Challenges team are organising as part of the European Conference on Artificial Intelligence (ECAI), which is hosted in Bologna, Italy from 25th–30th October 2025. […]
PANGAEA is a new evaluation and benchmarking protocol that covers a diverse set of datasets, tasks, resolutions, sensor modalities, and temporalities. This challenge is open-ended, giving participants the flexibility to explore, experiment, and iterate on their models over time. Alongside the open challenge, we’ll be launching regular Data Sprints – short, high-impact tasks that run for about two months each and focus on specific use cases within the PANGAEA dataset. These sprints will come with their own goals, deadlines, and prizes. […]
ESA Φ-lab Challenges, an initiative from Φ-lab of ESA’s Directorate of Earth Observation Programmes, strives to bring the worlds of AI and EO closer to encourage interaction and collaboration.
When large amounts of data is captured by remote sensing devices on EO satellites, our computers and AI algorithms can be used to help us solve problems. They can learn to recognise patterns and find correlations that humans would otherwise miss.
EO data allows us to gather global information about our planet Earth’s physical, chemical and biological systems via satellites carrying remote sensing devices.
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