# SebLague/Procedural-Landmass-Generation

Procedural Landmass Generation in Unity

Repository: https://github.com/SebLague/Procedural-Landmass-Generation
Canonical: https://ross.abutalabs.com/products/procedural-landmass-generation
Homepage: https://www.youtube.com/playlist?list=PLFt_AvWsXl0eBW2EiBtl_sxmDtSgZBxB3
Language: C#
License: MIT
License Family: permissive
Last push: 2024-01-12T08:26:42+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3861, "days_push": 964, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1236, forks 590 (observed 2026-08-28T04:04:05.260990+00:00)

## What it is
Source code for a Unity tutorial series on procedural landmass/terrain generation, written in C#. It accompanies Sebastian Lague's YouTube series and demonstrates noise-based terrain mesh generation.

## Use cases
- learn procedural terrain generation in Unity
- generate infinite landmass from noise
- study mesh generation with Perlin noise
- follow along with a terrain generation tutorial
- build a heightmap-based map generator

## When to choose
- you want to learn procedural terrain generation in Unity
- you prefer code paired with video tutorials
- you need a reference implementation of noise-based landmass generation

## When to avoid
- you need a production-ready terrain engine
- you don't use Unity or C#
- you want a maintained library with API guarantees

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: simulation, graphics, game-engine
- domain: simulation, tutorials
- platform: game-engine, cross-platform
- tags: unity, procedural-generation, terrain, noise, tutorial-series, csharp, game-development

## Member repositories
- SebLague/Procedural-Landmass-Generation (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:05.260990+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T08:21:56.758040+00:00, confidence not recorded.
  - readme: https://github.com/SebLague/Procedural-Landmass-Generation (fetched 2026-08-28T04:04:05.260990+00:00, sha 811f38dae8d6)
  - homepage: https://www.youtube.com/playlist?list=PLFt_AvWsXl0eBW2EiBtl_sxmDtSgZBxB3 (fetched 2026-08-29T12:21:07.588243+00:00, sha 44136fa355b3)
- Data as of 2026-08-30T08:39:29.467469+00:00.