  Data fusion is a well-known technique, becoming more and more popular in the
Artificial Intelligence for Earth Observation (AI4EO) domain mainly due to its
ability of reinforcing AI4EO applications by combining multiple data sources
and thus bringing better results. On the other hand, like other methods for
satellite data analysis, data fusion itself is also benefiting and evolving
thanks to the integration of Artificial Intelligence (AI). In this letter, four
data fusion paradigms, based on Convolutional Neural Networks (CNNs), are
analyzed and implemented. The goals are to provide a systematic procedure for
choosing the best data fusion framework, resulting in the best classification
results, once the basic structure for the CNN has been defined, and to help
interested researchers in their work when data fusion applied to remote sensing
is involved. The procedure has been validated for land-cover classification but
it can be transferred to other cases.
