i hit up google scholar occasionally looking for references, but literally everything seems to be applying them to 2D images.
What's even the difference between 1D inputs and 2D inputs? It's all a bunch of numbers anyway. I don't think it really matters if the pixels are arranged (as you see them) in a neat rectangle vs in a straight line. You could take a 2D matrix and enumerate it as a linear string of numbers and it would still be the same matrix, just represented differently. I don't think the CNN cares either way.
I would go as far as saying that the 1D-ness of the input is just "in your head".
If you arbitrarily represent a signal as a 2D matrix, then abrupt changes in the gradient on the vertical axis are meaningless. But the same is not true in an image, which is naturally represented as a 2D matrix. Here, a sudden change on the vertical axis usually corresponds to an edge in the image.
If you represent an image as a 1D array, you throw away spatial information. So I'm not sure about the 1D-ness just being in ones head.
edit: I'm not sure if you're asking specifically for examples of CNNs applied to linear image sensor data, or if you're asking whether CNNs have been applied to any 1D input data.