Building Confidence in Earth Observation

Satellite data can be affected by changing conditions, different landscapes can share similar characteristics, and even human experts may interpret the same information differently. Rather than hiding this uncertainty, the next generation of Earth observation models needs to make it visible.

Instead of providing a single prediction with an implied level of certainty, models can identify situations where several outcomes are possible. This gives users a clearer understanding of where predictions are reliable and where additional information may be needed.

By combining Earth observation data with foundation models, self-supervised learning, Graph Neural Networks, causal methods and explainable AI, ThinkingEarth is working towards a more connected and intelligent understanding of our planet.

For applications ranging from forests and cities to food security and energy, knowing when a model is uncertain can be just as valuable as knowing its prediction.

The future of Earth observation is therefore not simply about generating more data or more accurate models. It is about building Earth intelligence that is transparent, reliable and useful for real-world decisions.