SUMMER SCHOOL: AI technologies for processing multi-source Earth Observation (EO) data

Join us for a 3-day summer school hosted by LIST, featuring top international experts delving into cutting-edge AI technologies for processing multi-source Earth Observation (EO) data.

In recent years, Earth Observation satellite systems have become increasingly accessible, opening up a wealth of opportunities for addressing pressing global issues like climate change and enhancing our understanding of our planet's complexities.

With the development of satellite constellations equipped with advanced sensors, Earth monitoring has become more frequent, precise, and detailed than ever before. The data captured by these satellites offer vast potential for various applications, thanks to their wide range of measurement frequencies and spatial resolutions reaching up to tens of centimeters.

However, simply having access to vast amounts of data isn't enough; it's crucial to translate this data into actionable insights that can drive meaningful societal change. Machine learning and deep learning techniques are instrumental in efficiently analyzing large datasets and uncovering new insights across various application domains.

Don't miss out on this opportunity to learn from experts and explore the transformative potential of AI in Earth Observation

*upon request Summer School participant will benefit from a special price to attend the LEO DAY conference.

Objectives

The objective of this summer school is to offer valuable insights into the application of machine learning and deep learning techniques for interpreting Earth Observation (EO) data.

The program comprises a series of theoretical lectures delving into the principles behind cutting-edge deep learning approaches, alongside practical sessions featuring use cases that highlight their potential and potential limitations within specific applications.

Hands-on workshops will provide practical guidance on setting up and deploying various machine learning models. Ultimately, the aim is to bridge the gap between deep learning methodologies and EO data challenges, optimizing the utilization of both technologies. Participants will benefit from the expertise of instructors spanning machine learning, deep learning, and EO domains.

Programme