This is a model that I made for a historical game. I wanted to have a 1:1 scale model of Europe, but my problem was that 100m data was too low-res while 10m LIDAR data was patchy, took hundreds of GBs to store and was full of manmade objects like mines, buildings and so on.
I trained this model on undeveloped landscape so that it can quickly add plausible erosion features, rocks, etc to the low-resolution height data and sort of reconstruct what the terrain would look like before any human interference.
On my phone but very interested in this (hence leaving a comment so I can find it later). What’s the variation, can we generate different maps from the same low res seed?
I’m interested in this because “macro maps” can be hand built in a way that may want to preserve gameplay balance while individual games can still feel broadly unique.
Thank you! Yes as well as the input image you can pass in a seed value (otherwise the result is deterministic). I hadn't tried it with pure noise rather than satellite data but it does pretty well: https://jgibbs.dev/assets/terrainsr-noise.png