DevLog 20250729: Procedural Terrain Generation

Intro

Procedural generation, often called algorithmic art, is the process of turning code into visual content. Unlike generative AI, which can often feel "either works or doesn't", procedural generation offers predictable results and detailed control over every element of the outcome. Unlike simulation, its process is less about modeling physical phenomena and more about manipulating data through arithmetic and logic.

In practical production, procedural generation rarely stands alone. It’s usually combined with hand-crafted elements to achieve a balance between efficiency and artistic vision - a good example is the diverse planets in No Man’s Sky, which mix algorithmic rules with human-made textures and assets. Unreal Engine’s City Demo is, in my opinion, one of the most condensed showcases of how far this paradigm can be pushed. Yet, as I will discuss in a future article, procedural generation also faces diminishing returns: the more we aim for total automation, the less room we leave for meaningful artistic decisions. For world-building (not just game design), it often makes more sense to focus on curated, high-quality content rather than endlessly expanding algorithmic variety.

How does it has to do with Divooka?

When we talk about general-purpose, low-level procedural generation, three tools come to mind: Houdini, Blender Geometry Nodes, and Rhino Grasshopper. All three embrace a dataflow mindset, where complex results emerge from chaining and manipulating small, modular operations.

At its core, procedural geometry is about transforming data points - vertices, edges, and faces - through parametric relationships. The same happens when working with a dataflow diagram: each node transforms data in a specific way, and the network of nodes collectively defines the outcome. This data-centric, rule-based mindset has a strong connection to what I envision for Divooka. It’s not just about "automating" art; it’s about building a flexible, modular pipeline where small algorithmic rules can shape a larger creative vision.

Procedural Terrain Generation

Among all categories of procedural content, terrain generation stands out as being easily feasible while also present just enough technical and artistic challenge. Procedural terrains, trees, and clouds - when done well - can be indispensable in environmental design. These elements create a sense of scale, variety, and believability that would be prohibitively expensive to craft by hand, especially at scale.

Procedural humans are another major frontier, as projects like MetaHuman have shown. However, most current solutions in this area remain asset-based (pre-designed components combined procedurally) rather than fully algorithm-driven, due to the complexity of human anatomy and subtle facial variations.

What makes procedural terrain generation truly interesting is its multi-scale nature. The same algorithms that define the curvature of a hill, the placement of a forest, or the erosion patterns of a mountain range can be scaled up as procedural rules to shape entire planets, as seen in projects like Outerra or the planet tech behind Star Citizen.

For terrains specifically, there are multiple approaches:

• Parametric modeling: Platforms like TerraGen and Vue provides drop-in procedural terrains that works at large scale, at the sacrifice of simulating natural erosion, weathering, and geology.
• Heightmap-based workflows: Tools like World Machine remain the gold standard here, providing artists with granular control over erosion layers, noise functions, and texturing.
• Hybrid artistic sculpting: Recent software such as World Creator blends procedural systems with direct, real-time sculpting, letting artists guide the procedural logic for more intentional results.
• Biomes and planet-scale world maps: Beyond local terrain detail, truly massive environments need a global "climate" layer to drive ecosystem placement and continental shapes. Typically you start by generating low-frequency noise on a sphere (using an icosphere or cubemap projection) to define elevation, temperature, and moisture fields. These three channels feed a biome classifier (for example, using Whittaker’s biome chart) to assign regions like tundra, desert, rainforest, or grassland. Then, by layering higher-frequency noise and erosion simulations, you carve coastlines and mountain ranges that respect those climate zones. On top of that, you can stream in progressively finer mesh tiles or voxel chunks as the viewer approaches, blending seamlessly from a planet-wide view down to individual hills and caves. This multi-scale, data-driven pipeline lets you create a consistent, believable world map—where a river born in snowy peaks will reliably flow toward warmer, wetter biomes, and deserts will hug the rain shadows of mountain ridges.
• Voxel and simulation-driven worlds: Games like Minecraft and Dwarf Fortress have shown that procedural generation can build entire, fully interactive worlds at massive scale, with layers of terrain below the surface.

Conclusion

Procedural generation is more than a tool - it’s a mindset, and a skill requiring practising. It allows creators to define rules and systems rather than individual assets, creating worlds that feel alive, infinite, and varied. However, its real power comes when balanced with handcrafted content, ensuring that algorithmic variety never comes at the cost of artistic intent.

For Divooka, the challenge (and opportunity) lies in embracing procedural workflows without losing sight of the content’s purpose. A beautiful world isn’t necessarily one that is infinitely generated - it’s one where the procedural tools empower the creator to focus on meaning, storytelling, and design.