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I am using a CNN for function approximation using geospatial data. The input of the function I am trying to approximate consists of all the spatial distances between N location on a grid and all the other points in the grid.

As of now I implemented a CNN that takes an "image" as input. The image has N channels, one for each location of interest. Each i-th channel is a matrix representing my grid, where the pixel values are the distance between each point in the grid and the i-th location of interest. The labels are the N values computed via the actual function I want to approximate. N can be up to 100.

**Here an example input of the first layer**:

So far I could see the train and validation loss go down, but since it is a bit of a unusual application for a CNN (to my knowledge the input channels are at most 3, RGB) I was wondering:

- does this many-channel-input approach have any pitfalls?
- will I be able to obtain a good accuracy or are there any hard limits I am not aware of?
- are there any other similar application in literature?

It is possible to train a CNN with multiple channels, for example i've seen Wavenet like structures applied to traffic datasets, where they have multiple channels such as: average speed, lane occupancy, number of cars etc. The only downside that I could see is the large parameter space that it might require. – razvanc92 – 2019-09-12T13:30:49.833