The project is dedicated to demonstration of Privacy Preserving Machine Learning (PPML) in EO data analytics as an emerging field in data science that strives to address protecting data privacy and confidentiality in a variety of machine learning applications. It is aimed to research and demonstrate secure frameworks for AI/ML, including cryptographic techniques such as …
The project aims at the development of algorithms for cross-calibration of hyperscout with S-2 and the development of the phi-sat 1 brain for cloud detection. The team is assessing Machine Learning methodology, techniques, and a suite of applications (ranging from Radiometric calibration of a single band L2A data, Calibration of data products, e.g. NDVI or …
The GRD4ML project aims to create an Enhanced Environment to generate Reference Data for the Retrieval of Geophysical Parameters by Machine Learning. Scientist and application developers will have the opportunity to create, enhance, utilise and manage their own datasets for training neural networks in a integrated environment based on free licensed tools and cloud computing …
An unprecedented richness of data is captured by satellites every day, resulting in an ever-growing time series of EO data. Despite the extensive availability of data, there are still many challenges when defining procedures for extracting relevant information from long time series. The goal of the AI4FOOD project is to tackle the challenges that arise …
The EO Open Data Science MOOC is hosted on EO-College with the title “Cubes and Clouds” https://eo-college.org/courses/cubes-and-clouds/. This course teaches the concepts of data cubes, cloud platforms, and open science in the context of Earth Observation. Its goal is to provide students with practical knowledge on how to effectively use EO Platforms for Open Data Science in the cloud. At the …
Digital Twins of the Earth address decision support under uncertainty, providing a prognostic, evidence-based decision support capability representing the dynamic relationships between the physical and natural environments (the Earth System) and society (such as socioeconomic pathways, adaptation and mitigation actions or governance). In accordance with this vision, the uses cases that may be envisioned for …
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 …
