Source-linked AI summary
MESA: Text-Driven Terrain Generation Using Latent Diffusion and Global Copernicus Data
Paul Borne--Pons, Mikolaj Czerkawski, Rosalie Martin, Romain Rouffet
TL;DR
Terrain modeling is difficult to scale and existing methods can struggle to capture landscape variety, while global high-resolution remote-sensing coverage is limited. MESA trains a text-conditioned diffusion model on global paired terrain and optical data, and qualitative experiments show diverse 2.5D terrain outputs; the authors report no existing text-based terrain benchmark.
Problem
Procedural and simulation terrain methods can be difficult to scale or lack realism and variety, while comprehensive high-resolution remote-sensing coverage of Earth’s landscapes is limited.
Method
MESA adapts Stable Diffusion 2.1 to generate paired optical images and elevation maps from captions, using global Major TOM data and geographically derived descriptors.
Results
Qualitative experiments show MESA generating diverse 2.5D optical and depth representations in response to text prompts specifying biome, geology, country, and season.
Takeaways & Limitations
The authors report promising performance for supporting creative pipelines that need terrain modeling, alongside an open global Copernicus DEM extension to Major TOM.
Takeaways & Limitations
There are no existing benchmarks for text-based terrain modeling, and the authors hope this work can act as a first step toward one.
Abstract
from arXiv · showhide
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.
1. Introduction
Terrain modeling remains complex and time-consuming, while procedural and simulation methods can struggle to scale or capture landscape variety. MESA explores a data-centric alternative, using global Earth-observation data to generate paired terrain and optical outputs from text.
- 1. Introduction: Procedural and simulation methods can be compute-expensive, lack realism, and fail to capture the variety of landscapes, making large-scale terrain modeling difficult.Terrain is fundamental to 3D outdoor scenes and central to video games and visual effects.
- 1. Introduction: MESA explores a data-centric approach to jointly model terrain surfaces and optical reflectance, drawing on generative models that learn patterns from real terrain data.The supplied passage says this approach can lead to high perceptual realism.
- 1. Introduction: MESA uses Stable Diffusion 2.1 and global, dense Major TOM data to generate diverse optical-image and elevation-map pairs from text captions.The paper also releases a global Copernicus DEM extension to Major TOM with open, free access.
2. Background
The background contrasts conventional terrain-generation limits with generative approaches, then reviews diffusion models, joint color-and-depth learning, and remote-sensing generation. The paper situates MESA within challenges of terrain variability, paired-modality modeling, and global landscape coverage.
- 2.1. Terrain Modeling: Procedural and simulation terrain methods can lack output variability, struggle at extreme scales, or incur rapidly growing compute costs; existing deep-learning methods focus on authoring existing terrain or elevation maps.Hybrid terrain types are difficult to synthesize when terrain types must be modeled explicitly.
- 2.2. Diffusion Models: Diffusion models learn to denoise noisy samples, while latent diffusion applies the process to VAE-compressed representations to reduce compute and memory costs.The noise target may be noise, the input image, or velocity, and denoising can be conditioned on side information such as text.
- 2.3. Diffusion models for joint learning of color and depth maps: Jointly generating RGB appearance and depth with one model requires adapting standard diffusion; prior work describes modality-specific outputs and links joint learning to more realistic outputs.The cited discussion notes that finetuning an existing latent-diffusion U-Net is relatively straightforward, while finetuning its VAE is more complex.
- 2.4. Diffusion models for remote sensing generation: Remote-sensing diffusion work has used ControlNets for spatially conditioned satellite imagery and scalar inputs for time-series tasks, but global high-resolution landscape coverage remains limited and costly.Remote-sensing imagery also varies substantially from natural images and across geographic regions.
3. Method
MESA combines a global dataset of reflectance, elevation, and captions with a Stable Diffusion-based model adapted to generate paired terrain modalities. Its method constructs training data and captions, then jointly denoises RGB and DEM latents while masking invalid or cloudy pixels.
- 3. Method: MESA is built from Major TOM visual samples, a Copernicus DEM extension, and captions, using a Stable Diffusion-based model adapted to 2.5D terrain.Major TOM Core-DEM is based on Copernicus DEM (GLO30), resampled to the grid-cell UTM projection with bilinear interpolation to reduce stairway artifacts while preserving relatively sharp details.
- 3.1.1 Major TOM: The released Major TOM Core-DEM dataset pairs global, dense Sentinel-2 and DEM coverage; no-data and cloud masks identify pixels excluded from training.The valid-pixel mask is the union of the no-data and cloud masks.
- 3.1.2 Captions from geographical coordinates: Captions are generated from each cell’s coordinates using country, biome, and local and regional geological descriptors; training prompts randomly drop descriptors and vary geological naming.The prompt template also includes the month.
- 3.1.2 Captions from geographical coordinates: The training set contains 1.3 million 10×10 km² 2.5D terrains, filtered for paired image and DEM visibility and excluding oceans, Antarctica, misaligned projections, and missing data.The paper describes the remaining cells as most of Earth’s land surface beyond Antarctica.
- 3.2. Terrain Generation: The model encodes RGB and depth with frozen VAEs, jointly denoises their independently noised latents through a caption-conditioned U-Net, and uses separate modality-specific output heads.A shared first DownBlock yields comparable results while reducing parameter count; a resized mask restricts loss backpropagation to valid, cloud-free pixels.
4. Experiments
The experiments are primarily qualitative, examining seed-driven diversity, prompt-descriptor effects, and shadow correction; the authors note that text-based terrain modeling has no existing benchmark.
- 4. Experiments: The experiments are mainly qualitative, and no existing benchmark for text-based terrain modeling is available.The authors present this work as a hoped-for first step toward such a benchmark.
- 4.1. Variablity and influence of seed: Different seeds yield diverse samples for a fixed caption while preserving similar caption-specified terrain characteristics.Table 2 places seeds in columns and fixed captions in rows.
- 4.2. Influence of captions: Biome and geological descriptors strongly shape terrain features and elevation, while country information has a milder effect and date conditioning can be inconsistent.Country-only prompts produced unrealistic, texture-like samples; biome and geology together were enough to generate realistic samples.
- 4.3. Influence of shadow correction: Shadow-corrected training reduced artifacts from Sentinel-2 L2A processing while producing terrains with similar characteristics.The comparison used independently trained variants, one trained on original L2A data and one on shadow-corrected L2A data.
5. Conclusion
MESA is a text-driven diffusion model trained on globally sampled Major TOM data to generate diverse terrain representations from text prompts. Qualitative experiments demonstrate prompt-controlled optical and depth outputs, with promising potential for creative pipelines.
- 5. Conclusion: MESA was trained on global Major TOM coverage, with 10 percent sampled randomly and retained for testing.The authors state that this coverage accounts for the range of terrain present on Earth.
- 5. Conclusion: MESA generates diverse 2.5D optical and depth terrain representations conditioned on biome, geological features, country context, and season.The conclusion reports that these capabilities were demonstrated in qualitative experiments.
- 5. Conclusion: The shadow-corrected MESA variant has superior visual quality, attributed to fewer artifacts in the training data.Table 4 compares two independently trained versions.
- 5. Conclusion: The authors report promising performance for supporting creative pipelines where terrain modeling is needed.The reported evidence is a series of qualitative experiments.