Challenge portfolio

AI for Earthquake Response

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

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. […]

EASi Workshop // HYPERVIEW2

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. […]

MapYourCity Challenge

Your city holds secrets ready to be revelead. We invite you to be part of this story and join the #MapYourCity challenge. […]

OrbitalAI 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. […]

AutoICE Challenge

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. […]

Sentinel 2 Challenge

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. […]

HYPERVIEW Challenge

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. […]

Food Security Challenge

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. […]

Read More… from Food Security Challenge

Air Quality & Health Challenge

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). […]

Why get involved?

Φ-lab Challenges is where the worlds of transformative innovation and Earth Observation meet with an enthusiastic community of talented users ready to solve your challenges through data competitions.

Together we define the parameters and users submit their algorithms. Our platform scores their accuracy in real-time on a leaderboard.

Competitions are a good way to tackle data problems, showcase your EO capabilities, and interact with people in a fun way.

We have the platform so you only need to bring the problem and the data to define the challenge!

Position yourself among the leading companies in transformative innovation & EO

Solve important problems for industry and society

Use a powerful innovation platform and get access to our passionate user community

Thanks to the ESA Φ-lab Challenges platform and the help of our partners, TUM, DLR, and Radiant Earth we stimulated the innovation in Spatio-temporal machine learning. With the help of ESA Φ-lab Challenges, a unique challenge was developed centred around modelling vegetation phenology from near-daily, harmonized time series of Planet Fusion and Sentinel Imagery. This furthered the developments of AI in Earth Observation.

Planet
Head of Marketing

The ESA ϕ-lab challenges platform has a powerful algorithm and functions which made our competition engaging. We meet many enthusiast participants with a desire to advance the field of hyperspectral image processing. This paved the way for the future of farming with the help of in-orbit processing.

KP Labs
Lead Scientist