Various texts on using CNNs for object detection in images talk about how their translation invariance is a good thing. Which makes sense for tasks where the object could be anywhere in the image. Let's say detecting a kitten in household images.
But let's say, you already have some information about the likely position of the object of interest in the image. For example, for detecting trees in a dataset of images of landscapes. Here in most cases, the trees are going to be in the bottom half of the image while in some cases they might be at the top (because it's on a hill or whatever). So you want your neural network to learn that information -- that trees are likely connected to the bottom part of the image (ground). Is this possible using the CNN paradigm?