In the specific case of knowing the location of the object in the image, one technique would be to crop and pad each training example so that the object is in the exact center. This way the extra information is passed to the neural network implicitly. This is how most face identification neural networks work.

If the "location" of the object is more abstract, like "bedroom" or "Spain," then I'd recommend concatenating the information to each pixel of the image. Don't be afraid to add a large number of extra input channels, neural networks handle this well. For example, Alpha Go has a 48 channel input layer.

https://medium.com/@jonathan_hui/alphago-how-it-works-technically-26ddcc085319

In the specific case of knowing the location of the object in the image, one technique would be to crop and pad each training example so that the object is in the exact center. This way the extra information is passed to the neural network implicitly. This is how most face identification neural networks work.

If the "location" of the object is more abstract, like "bedroom" or "Spain," then I'd recommend concatenating the information to each pixel of the image. Don't be afraid to add a large number of extra input channels, neural networks handle this well. For example, Alpha Go has a 48 channel input layer.