TerrainSR
fast, realistic heightmap upscaling model
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This is 1 of 192 launches in 3D design and spatial visualization tools — see how it stacks up on momentum and crowding →
102 other launches read as similar to this one →
Details
- External ID
- 49986740
- Source
- HN
- Company
- —
- Product
- TerrainSR
- Website domain
- huggingface.co
- Launched
- Oct. 7, 2026
- Cohort
- —
- Upvotes
- 27
- Upvotes percentile
- 0.7350993377483444
- Tags
- —
- Fetched at
- Oct. 9, 2026, 1:01 a.m.
- Updated at
- Oct. 9, 2026, 1:01 a.m.
Description
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.
Enrichment
- Niche
- 3D design and spatial visualization tools
- Vertical
- Media & entertainment
- Function
- Content generation
- Audience
- Developer
- AI stance
- AI-native
- Project type
- Hobby / open-source project
- Normalized one-liner
- neural upscaling model for terrain heightmaps
- Manually corrected
- False
Could you build this?
No Training custom super-resolution diffusion models on geospatial elevation data requires deep machine learning research expertise, large GPU compute resources, and domain-specific GIS data curation.
What it would actually take: The project requires sourcing, preprocessing, and filtering hundreds of gigabytes of geospatial elevation datasets (SRTM, LIDAR) to remove manmade artifacts, followed by designing a custom super-resolution diffusion or GAN network architecture in PyTorch. Training requires multi-GPU clusters, loss function tuning tailored to topography, and exporting optimized ONNX/TensorRT runtimes for game engine consumption.
Discussion
2 comments analyzed.
Feature requests: generate variations from the same low-res seed
Competitors
Other products that read as similar to this one — 102 launches clear the similarity bar, closest 8 shown.
Attention rank: #36 of 103 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 338 days after the earliest competitor.
- Feature detection exploration in Lidar DEMs via differential decomp · hn · 2026-01-01 · 8 upvotes · similarity 0.50
- Made a website for downloading EU and US LiDAR scans and terrain models · hn · 2026-07-13 · 5 upvotes · similarity 0.43
- WebGCM · hn · 2026-09-21 · 6 upvotes · similarity 0.41
- NormalMap · ph · 2026-09-14 · 1 upvotes · similarity 0.41
- TerraShift: What does +2°C (or -20°C) look like on Earth? · hn · 2026-03-17 · 5 upvotes · similarity 0.41
- My local climbing gym from photogrammetry · hn · 2026-08-10 · 29 upvotes · similarity 0.41
- Experiments with Weather Data · hn · 2026-07-29 · 6 upvotes · similarity 0.41
- topowall · github · 2026-09-13 · 62 upvotes · similarity 0.39
Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a content generation tool for Government yet.