Atmospheric Phenomena and Climate Variability

How might AI improve climate models and enable better decision making for resilience planning?

CNN-based approaches are essential for exploiting the long history of Earth observations to evaluate natural variability and secular trends in cloud characteristics. Developing a semi-supervised analysis framework that builds a neural network model could exploit this data richness.

This procedure would start by training a CNN classifier on labeled and deterministically defined cloud classes, learning the partitioning of classes, and analysing variability within each separate class by using a variational auto encoder to find candidates for new cloud classes.

If new class candidates appear, representative images would be synthesised for each new class and input as sources to the supervised CNN approach. This procedure would be iterated to reach a new partition configuration that accommodates the new classes. The framework could also be extended to integrate information from multiple satellites over decades.

Disaster Prevention, Progress and Response

How might we utilise AI and Earth observation data to support improved decision making to protect the planet?

Working closely with our partners at UNICEF, can we investigate how AI can improve our capabilities to forecast and respond to floods using orbital imagery, coupled with ground observations and social data?

GROUND STATION PASS OPTIMIZATION FOR CONSTELLATIONS

How might AI be used to further optimise spacecraft operations?

During routine operations of existing ESA missions, a large amount of time is invested in the scheduling and planning of ground station passes for satellites. This is mainly with some automation and manual work and involves trial and error. Can we create an optimised ground station schedule for spacecraft using AI?
Ideally, such a solution would reduce the amount of tracking hours while maximising the science return.