How can you include information not present in an image for neural networks?
Other answers suggest to put an additional channel, I disagree. I think it's a very computationally intensive, time consuming process. Moreover, it forces non-pixel data to be processed by Conv filters, which doesn't make much sense IMHO.

I suggest you to establish a multi-input model. It would be composed by three parts:


A Convolutional part, to process pixel data,
A Feed-forward part to process non-image data,
Another Feed-forward part that elaborates the prediction based on the concatenation of the two outputs above.


You will need to instantiate them separately, then combine together in a Keras Model(). You will also need Concatenate() layers to combine the two different sources of data.

You can read more about the implementation of multi-input Neural Networks here.