Wind power resources in mountainous regions are conditioned on a vast variety of factors influencing air flow. Complex topography causes various phenomena such as localised thermal winds, acceleration due to tunneling and Foehn winds interfering at a range of spatial scales and varying in time due to weather seasonality. It increases the dimensionality of parameter space and adds additional complexity to sampling strategies and monitoring network design for wind resource assessment and location allocation for wind turbines. This work explores an active learning approach to multiple kernel learning (MKL) to explore the highdimensional space of topographic features influencing wind speeds. MKL allows handling spatial heterogeneity and non-stationarity while providing physically interpretable data-driven models useful for decision support. Our results on real data from the Swiss Alps suggest the efficiency of MKL both for feature selection, predictive modelling and sampling design, also showing that care has to be taken to avoid over-fitting by over-localised terms in kernel dictionaries.