  In this work we demonstrate the possibility of estimating the wind
environment of a UAV without specialised sensors, using only the UAV's
trajectory, applying a causal machine learning approach. We implement the
causal curiosity method which combines machine learning times series
classification and clustering with a causal framework. We analyse three
distinct wind environments: constant wind, shear wind, and turbulence, and
explore different optimisation strategies for optimal UAV manoeuvres to
estimate the wind conditions. The proposed approach can be used to design
optimal trajectories in challenging weather conditions, and to avoid
specialised sensors that add to the UAV's weight and compromise its
functionality.
