Hi! I’ve recently delved into the world of ML. I’m sure this must’ve be a very simple or foolish question but I’ve not found a good answer to this. :’(
I have a piece of code that generates JSON representing maps for a game.
[
  { x1: 0, y1: 0, x2: 10, y2: 3, type: 'GRASS' },
  { x1: 3, y1: 2, x2: 8, y2: 3, type: 'ROCK' },
  { x1: 0, y1: 0, x2: 10, y2: 3, type: 'WATER' },
  ...
]

Each map representation is composed of a list of elements with different bounding boxes and a type of element that occupies that area. Furthermore, the map is not tile-based, meaning I cannot use a 2-D matrix of type values as a representation. Lastly, as the map is not tile-based, the number of elements in the map JSON can be arbitrary (although there is an upper limit to this number). I have a ton of these JSONs and I’ve manually classified them into groups based on similarity.
I was wondering if it was possible to devise an ML model that would be capable of processing this sort of input data and computing similarity scores between two maps. Most tutorials I’ve gone through either talk about simple tensors (XOR) or images, with nothing in between.
As a last resort, I can use the JSON to generate an image of the map itself, where type might be encoded as different color or texture. But I think (please correct me if I’m wrong) that the JSON represents the map image more simply and it a NN trained on JSON might perform better than one trained on generated images. In the JSON, the NN should know that there are only 5 parameters to deal with. On the other hand, in an image, does it consider the shape of the tiles, or does it consider the color, or texture, or positioning, or number of corners, or a whole host of other parameters.
I believe a NN trained on JSON should perform better than one trained on images. But is there a way to transform this JSON into a format that is compatible with a NN? The main problem I’m facing is that even though each element in the JSON has exactly 5 fields, but the number of elements is not constant.
I can try padding, but that would impose a rigid upper limit and also force the input layer to have way more neurons than would be required in most cases.
Newbie here. Please be gentle. 
Asim