Title: MESA: Text-Driven Terrain Generation Using Latent Diffusion and Global Copernicus Data Show Chinese title

Title: MESA：基于潜在扩散和全球Copernicus数据的文本驱动地形生成

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. Show Chinese abstract

Abstract: 地形建模传统上依赖于过程技术，这些技术通常需要大量的领域专业知识和手工制作的规则。在这篇论文中，我们提出了MESA——一种新颖的数据为中心的替代方案，通过在全球遥感数据上训练扩散模型来实现。这种方法利用大规模地理空间信息，从文本描述中生成高质量的地形样本，展示了地形生成的一种灵活且可扩展的解决方案。通过广泛的实验展示了该模型的能力，突出了其生成逼真且多样化的地形景观的能力。为了支持这项工作而产生的数据集，Major TOM Core-DEM扩展数据集，作为全球地形数据的综合资源公开发布。结果表明，基于数据的模型，如果用遥感数据训练，可以为逼真的地形建模和生成提供强大的工具。