The AI4EO Accelerator bridges the gap between Artificial Intelligence and Earth Observation by connecting partners in industry, national and international organisations and the third sector with students on short innovative projects. Project themes are selected for their value, impact, feasibility, and relevance to earth observation research. The AI4EO Accelerator is a collaboration between ESA Φ-Lab …
The SeasFire project, funded by the ESA, is taking an innovative approach to predicting seasonal wildfire patterns in Europe. SeasFire uses cutting-edge technology, such as modern deep learning models and Earth Observation data, to explore the spatio-temporal connections between atmospheric conditions and fire regimes to gain valuable insights into predicting potential wildfires. The project aligns …
Quantum Computing (QC) is rapidly emerging as an alternative to traditional classical computing thanks to its potential advantages in terms of representational power and computing time. This activity targets to study the application and explore the potential of Quantum Machine Learning (QML) on realistic Earth Observation (EO) use cases. Several EO scenarios are investigated. A …
Farmers and field owners need information about the soil parameters to optimize the fertilization process. This may ultimately lead to selecting a better mix of fertilizers, and to reducing the overall amount of them. The current approach toward quantifying the soil parameters (e.g., macroelements) is very user-dependent, laborious, time-consuming, vastly manual – we have to …
The project aims to bring robust, accountable, and scalable multi-spectral super-resolution techniques to the Earth Observation (EO) community for the ubiquitous L2 and L3 pre-processing of the Sentinel-2 (S2) revisits archive. Super-resolution (SR) is a nascent technology and the roadmap to maturity will require insights from many disciplines. Super-resolution is not just about image generation, …
The aim of this project is to develop and validate novel EO-based hyperspectral data generated by an unprecedented miniaturized hyperspectral sensor (HyperScout-2) employed in the ESA EO directorate small sat mission PhiSat-1. The processed data output of this project will advance existing EO capabilities. The processing and the distribution of the data to the community …
The urban environment we live in is more and more complex. Interrelations and dependencies as well as the effects of changes are becoming increasingly difficult to assess. In this framework, the Digital Twin Urban Pilot (DTUP) concept is of key importance: It allows to create an image of the reality on which one can effectively …
The main objective of the RepreSent project is to capitalize on the potential of artificial intelligence (AI) and Earth observation (EO) by exploiting the non-supervised learning paradigms. In this context, it is essential to come up with non-supervised learning-based solutions for impactful use cases that use unlabeled EO data. The project started on April 1st, …
FluViSat is a proof-of-concept study that demonstrates the potential of satellite-collected video imagery to provide accurate and timely quantification of water movements and river flows for the benefit of water management globally. The study is being led by the UK Centre for Ecology and Hydrology, in partnership with the Luxembourg-based company, RSS-Hydro, and the Queensland …
In the frame of the CORTEX project, AGENIUM Space develops a set of solutions to simplify high performance Deep Neural Networks to make possible AI analysis on-board. AGENIUM Space reduces networks complexity and optimize DNN execution in on-board hardware (SoC FPGA). The company evaluates different solutions to prepare on-board analysis before launch, as training models …
