Here’s what my data look like:

• There is a collection of a few thousand objects
• Each object is sampled on a grid, with multiple properties observed per point
• There are two types of objects

And the goal is to identify which of the two types of objects we have given the grid of samples.

So this looks like a straightforward CNN project. I’m not an expert, but I have experience fitting those on RGB images using TF/keras and doing some model tuning and validation. Should be easy, right?

The problem is that these grids are all irregular. I know how to fit CNNs when I have a collection of rectangular images all of the same size (if they’re different dimensions, then it’s straightforward to resize/interpolate/resample them). But how do you handle grids taken from what I can best describe as “blobs”? To make things even harder, the sampling instrument occasionally malfunctioned, so I have occasional NAs for the measurements. As far as my domain knowledge goes, the shape of the grid or the frequency of the instrument failing to make a valid measurement shouldn’t depend on the classification (or vice versa).

My thinking is perhaps there’s a way to reshape everything into rectangles of the same size, but I can’t think of how. Or is there another totally different type of model that would work best for these data?