I am looking into a problem wherein the whole geographic area is divided into number of bins/pixels so we get nxn matrix covering whole region. Now each bin/pixel has number of parameters/features associated with it e.g., number of buildings in that bin, number of people living in that bin, poverty level of that bin, crime level in that bin, etc. This whole information represents one sample of the training dataset i.e., we have this matrix like data for different geographic regions with corresponding labels. How to best handle this kind of dataset for machine learning task e.g., ML trained on some nxn grids for different areas like this will classify labels for some unseen test nxn grids. I am thinking that in term of CNNs, it may be represented in terms of channels so each channel represents associated features of a bin. Whats your suggestion?