  Understanding the behaviour of environmental extreme events is crucial for
evaluating economic losses, assessing risks, health care and many other
aspects. In the spatial context, relevant for environmental events, the
dependence structure plays a central rule, as it influence joined extreme
events and extrapolation on them. So that, recognising or at least having
preliminary informations on patterns of these dependence structures is a
valuable knowledge for understanding extreme events. In this study, we address
the question of automatic recognition of spatial Asymptotic Dependence (AD)
versus Asymptotic independence (AI), using Convolutional Neural Network (CNN).
We have designed an architecture of Convolutional Neural Network to be an
efficient classifier of the dependence structure. Upper and lower tail
dependence measures are used to train the CNN. We have tested our methodology
on simulated and real data sets: air temperature data at two meter over Iraq
land and Rainfall data in the east cost of Australia.
