AI4EO is an initiative from the Φ-lab of ESA’s Directorate of Earth Observation Programmes. The Φ-lab’s mission is to accelerate the future of earth observation, by helping Europe’s earth observation and space researchers and companies adopt disruptive technologies and methods.
The AI4EO initiative looks to foster innovation in Earth Observation (EO), through the application of Artificial Intelligence (AI) techniques. The impact of AI in this domain has large environmental, societal, and economic implications, and the merging of these two domains can yield in actionable insights for scientists, as well as political and economic decision makers.
Developments in digital technologies and our capability to monitor our home planet with Earth Observation (EO) satellites have led to new opportunities for science and business. There is an increasing need to mine the large amounts of data generated by the new generation of satellites coming online, including Copernicus and New Space satellites. Artificial Intelligence (AI) is an important part of the solution, enabling scalable exploration of big data and bringing new insight and predictive capabilities.
AI4EO aims to build a community connecting AI and EO experts, innovators, and researchers to build EU-wide capacity, and explore the wider potential of combined applications through a mechanism of data challenges.
Planetek Italia has the technical responsibility of the project; it is also responsible for the task related to the developing and maintaining the crowdsourcing platform. Moreover, Planetek supports its project partners in the management and organization of the various challenges for the AI and EO communities.
Participants of the challenges will be part of the AI4EO community and will access the AI4EO platform together with resources and tools.
#AI4AirQuality - The AI4EO Air Quality & Health challenge (closed)
The AI4EO Air Quality & Health challenge stems from a need expressed by ECMWF and the Copernicus Atmosphere Monitoring Service (CAMS) and focuses 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).
Participants are challenged to develop new methods to downscale air quality products to a resolution that can be used at a local level to deliver surface concentration maps of PM2.5 and NO2 of a higher resolution than Sentinel-5P satellite data and the CAMS products. A quantitative and qualitative evaluation of the results will be performed over the three selected Areas of Interest: Northern Italy, California and South Africa/Rwanda.
#AI4Sentinel2 - Agriculture (Start June 2021 - end November 2021)
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. Accurate and reliable agricultural data is therefore paramount to ensure food security and global subsistence.
The goal of this challenge is to map cultivated land using Copernicus Sentinel imagery and to develop solutions to extract as much information as possible from the native 10-meter per pixel resolution. Your aim will be to identify agricultural areas smaller or narrower than a Sentinel-2 pixel. Area of Interest is Slovenia.