A modular and extensible library for parameterized terrain generation
Erik Wallin

TL;DR
This paper introduces a modular, Python-based library for procedural terrain generation that allows for customizable, reproducible, and scriptable terrains suitable for machine learning and perception tasks.
Contribution
The authors present a flexible, extensible terrain generation framework that supports parameterization, integration with Blender, and application-specific customization.
Findings
Supports complex, parameterized terrains with fine control over features
Enables reproducibility and variation for automated workflows
Integrates with Blender for rendering and object placement
Abstract
Simulation-driven development of intelligent machines benefits from artificial terrains with controllable, well-defined characteristics. However, most existing tools for terrain generation focus on artist-driven workflows and visual realism, with limited support for parameterization, reproducibility, or scripting. We present a modular, Python-based library for procedural terrain generation that enables users to construct complex, parameterized terrains by chaining together simple modules. The system supports both structured and noise-based terrain elements, and integrates with Blender for rendering and object placement. The framework is designed to support applications such as generating synthetic terrains for training machine learning models or producing ground truth for perception tasks. By using a minimal but extensible set of modules, the system achieves high flexibility while…
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Taxonomy
Topics3D Modeling in Geospatial Applications
