MESA: Text-Driven Terrain Generation Using Latent Diffusion and Global Copernicus Data

• URL: http://arxiv.org/abs/2504.07210v2
• Date: Mon, 14 Apr 2025 17:25:41 GMT
• Title: MESA: Text-Driven Terrain Generation Using Latent Diffusion and Global Copernicus Data
• Authors: Paul Borne--Pons, Mikolaj Czerkawski, Rosalie Martin, Romain Rouffet,
• Abstract summary: We present MESA - a novel data-centric alternative to procedural terrain modeling.<n>MESA generates high-quality terrain samples from text descriptions using global remote sensing data.<n>The model's capabilities are demonstrated through extensive experiments, highlighting its ability to generate realistic and diverse terrain landscapes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Terrain modeling has traditionally relied on procedural techniques, which often require extensive domain expertise and handcrafted rules. In this paper, we present MESA - a novel data-centric alternative by training a diffusion model on global remote sensing data. This approach leverages large-scale geospatial information to generate high-quality terrain samples from text descriptions, showcasing a flexible and scalable solution for terrain generation. The model's capabilities are demonstrated through extensive experiments, highlighting its ability to generate realistic and diverse terrain landscapes. The dataset produced to support this work, the Major TOM Core-DEM extension dataset, is released openly as a comprehensive resource for global terrain data. The results suggest that data-driven models, trained on remote sensing data, can provide a powerful tool for realistic terrain modeling and generation.

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