Training a model that has both 2D and 1D features using a CNN

I’m looking to pre-train a model for an RL agent but I’m having some trouble figuring some stuff out.

Dataset: Minerl MineRLNavigateDense-v0

The observation space includes :

2D screen input (64,64) (+ 3 channels of color)
1D (scalar) compass angle
1D (scalar) number of dirt blocks
+ this is all over time.

I am also given the reward based on the action the human took.

When training a model using a CNN for time series classification my understanding is that each feature (out of k total features) at a given point in time can be represented as a scalar value (visualized as each box in each row) and then the “time” component can be visualized by each row being a new timestep:

My question is:
How do you set up the training data in such a way where this sort of CNN can be used to train on, particularly because each datapoint has a different dimension, do we just flatten the screen input into 64*64*3=12288 new features? Because that feels wrong.

UPDATE:
My first problem, combining the 2D image and the scalar data points was answered on Discord group I am in. In Keras at least, there’s these special layer types called “merge layers” https://keras.io/layers/merge/ A CNN can be applied to the image first, then merged with the scalar data, this could then be passed into a 1D CNN to add the temporal component, however I haven’t actually done any of this yet. 🙂