I suspect you recognize dimensionality reduction, but to reiterate for my own understanding: t-Distributed Stochastic Neighbor Embedding (t-SNE) is one method among a few other, (more popular?) ones like Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP).

Is t-SNE the most appropriate technique for modeling the terrain under a multidimensional "walk"? Possibly a linear technique (PCA, LDA, SVD?) or PaCMAP[1], which "dynamically employs a particular set of mid-near pairs to capture the global structure and then improve the local structure." (Qattous H, 2023)

0. https://qiao.github.io/PathFinding.js/visual/

1. https://pmc.ncbi.nlm.nih.gov/articles/PMC10756978/

Edit: for reference, the tensor projector: https://projector.tensorflow.org/