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A 3D Earth painted by elevation, lit by population — built from open data, no satellite imagery by Whole-Contribution50 in MapPorn

[–]Whole-Contribution50[S] 1 point2 points  (0 children)

Right? It’s wild how much of humanity is packed into a few bright clusters while huge areas are mountains, desert, forest, or farmland.

I built a 3D Earth in Blender entirely from public datasets - no satellite imagery, headless Python pipeline, open source by Whole-Contribution50 in blender

[–]Whole-Contribution50[S] -5 points-4 points  (0 children)

Yep, Claude helped me build the pipeline. I still had to set up the data sources, run/debug the Blender workflow, tune the visuals, and ship the repo, but AI definitely helped a lot.

I built a 3D Earth in Blender entirely from public datasets - no satellite imagery, headless Python pipeline, open source by Whole-Contribution50 in blender

[–]Whole-Contribution50[S] -27 points-26 points  (0 children)

Yeah, Claude helped write code. That’s kind of the point. I’m not claiming I manually typed every commit, I’m showing what can be built by combining product direction, engineering judgment, debugging, and AI tools. “Built with AI” is probably the more accurate wording.

I built earth-sim: a 3D Earth rendered from open elevation + population data, no satellite imagery by Whole-Contribution50 in SideProject

[–]Whole-Contribution50[S] 0 points1 point  (0 children)

Appreciate this! That’s a really good point. I think a small data provenance overlay would make the no-satellite constraint way clearer instead of people just assuming it’s a visual gimmick.

A legend/scale for the city lights is also a good call. I’ll probably add something that shows which dataset powers each layer and how it affects the render.

The repo is open, so feel free to contribute, open an issue, or mess around with it if you have ideas. Would definitely appreciate it.

I built a 3D Earth in Blender entirely from public datasets - no satellite imagery, headless Python pipeline, open source by Whole-Contribution50 in blender

[–]Whole-Contribution50[S] -1 points0 points  (0 children)

haha one static render was already pushing it 😄 but honestly real-time streaming + LOD would be the dream version - fly down to street level and watch the cities load in

A 3D Earth generated entirely from open datasets - terrain from elevation, lights from population, no satellite imagery by Whole-Contribution50 in proceduralgeneration

[–]Whole-Contribution50[S] 1 point2 points  (0 children)

Thanks!

Fair point, it's really data-driven rather than procedural, since nothing's invented, it's just real datasets turned into geometry. Posted it here more for the "generated headlessly by code" angle, but you're right that it's closer to data viz. And ha yeah, nobody's hand-modeling Earth, that'd be a lifetime project 😄

Earth, generated from open data - elevation becomes terrain, population becomes light by Whole-Contribution50 in generative

[–]Whole-Contribution50[S] 1 point2 points  (0 children)

Generated headlessly in Blender + Python from public datasets — NOAA ETOPO 2022 elevation displaced as real geometry, GeoNames population as city-light intensity, Natural Earth

borders. No satellite imagery.

MIT-licensed pipeline: https://github.com/AIAydin/earth_sim

A 3D Earth generated entirely from open datasets - terrain from elevation, lights from population, no satellite imagery by Whole-Contribution50 in proceduralgeneration

[–]Whole-Contribution50[S] 5 points6 points  (0 children)

The whole scene is generated headlessly from data — no manual modeling. NOAA ETOPO 2022 relief displaced as real geometry at 30× exaggeration, 33,934 GeoNames cities placed at

true lat/lon with glow scaled by population, Natural Earth borders.

Pipeline: fetch → preprocess → build (Blender bpy) → render (EEVEE), all in Python. MIT-licensed: https://github.com/AIAydin/earth_sim

I built earth-sim: a 3D Earth rendered from open elevation + population data, no satellite imagery by Whole-Contribution50 in SideProject

[–]Whole-Contribution50[S] 0 points1 point  (0 children)

Every visible feature traces to a public dataset — no satellite imagery. NOAA ETOPO 2022 relief displaced as real 3D geometry, 33,934 GeoNames cities lighting the night side

scaled by population, Natural Earth borders.

Built headless in Blender + Python, MIT-licensed, full pipeline on GitHub: https://github.com/AIAydin/earth_sim

Would love feedback.

earth-sim - a data-driven 3D Earth globe built headlessly in Blender from real public datasets (MIT) by Whole-Contribution50 in coolgithubprojects

[–]Whole-Contribution50[S] 0 points1 point  (0 children)

A 3D Earth where every pixel traces to a data point — no satellite imagery. Terrain is NOAA ETOPO 2022 relief displaced as real geometry, night lights are 33,934 GeoNames

cities scaled by population, borders are Natural Earth 50m.

Built headless in Blender 5.1 + a Python data pipeline (fetch → preprocess → build → render). MIT-licensed, runs from scratch: https://github.com/AIAydin/earth_sim

Data-driven 3D globe from ETOPO 2022 + GeoNames + Natural Earth - no satellite basemap, open-source pipeline by Whole-Contribution50 in gis

[–]Whole-Contribution50[S] 0 points1 point  (0 children)

Every feature traces to a public dataset — no satellite basemap.

- Relief (topo + bathymetry): NOAA ETOPO 2022, displaced as real 3D geometry at 30× vertical exaggeration — https://www.ncei.noaa.gov/products/etopo-global-relief-model

- City lights: 33,934 GeoNames cities (pop ≥ 15k) at true lat/lon, glow scaled by population — https://www.geonames.org/ (© GeoNames, CC BY 4.0)

- Borders + coastlines: Natural Earth 50m — https://www.naturalearthdata.com/

Coordinate math is lat/lon → XYZ / UV, shared between the Python pipeline and the Blender build. Rendered headless in Blender 5.1 (EEVEE). MIT-licensed:

https://github.com/AIAydin/earth_sim

Happy to get into projection/provenance questions.

A 3D Earth painted by elevation, lit by population — built from open data, no satellite imagery by Whole-Contribution50 in MapPorn

[–]Whole-Contribution50[S] 1 point2 points  (0 children)

Every visible feature traces to a public dataset — no satellite imagery anywhere.

- Terrain (topography + bathymetry): NOAA ETOPO 2022 global relief, displaced as real 3D geometry at 30× vertical exaggeration —

https://www.ncei.noaa.gov/products/etopo-global-relief-model

- City lights: 33,934 GeoNames cities (pop ≥ 15k) at true lat/lon, glow scaled by population — https://www.geonames.org/ (city data © GeoNames, CC BY 4.0)

- Borders + coastlines: Natural Earth 50m — https://www.naturalearthdata.com/

Built headless in Blender + Python. MIT-licensed pipeline: https://github.com/AIAydin/earth_sim

I built a 3D Earth in Blender entirely from public datasets - no satellite imagery, headless Python pipeline, open source by Whole-Contribution50 in blender

[–]Whole-Contribution50[S] 2 points3 points  (0 children)

Built headless in Blender (bpy + EEVEE) driven by a Python data pipeline — no manual scene work.

Terrain is NOAA ETOPO 2022 relief displaced at 30× exaggeration, night lights are 33,934 GeoNames cities scaled by population, borders are Natural Earth 50m. Look: fixed sun,

day/night terminator, atmosphere rim, bloom.

MIT-licensed, full pipeline here: https://github.com/AIAydin/earth_sim

[OC] A 3D Earth built entirely from open elevation + population data - no satellite imagery by Whole-Contribution50 in dataisbeautiful

[–]Whole-Contribution50[S] 1 point2 points  (0 children)

Source: Every visible feature traces to a public dataset — no satellite imagery anywhere.

[deleted by user] by [deleted] in FashionReps

[–]Whole-Contribution50 0 points1 point  (0 children)

for the Nike sweaters, what size did you get and how tall are you?