Generating Historical Maps with AI: Custom Diffusion Models for an R&D Project
3x
40%
20,000+
Services provided
AI research and custom development
Fine-tuning diffusion models
Dataset engineering
About the Client
The client is an innovative organization specializing in creating digital tools for visualizing historical events. Their goal is to develop a platform that enables users to generate precise maps of any historical period based on textual descriptions.
Challenge
Usually, standard diffusion models—such as Stable Diffusion—struggle with map generation:
hours per week
a private equity agent spends for market research and networking
Inaccurate geographical borders
Artifacts and low detail
Incorrect representation of historical events (conquests, border changes)
Text prompt length limitations (up to 77 tokens)
Solution
The Anadea team joined the client as a full-cycle R&D partner, delivering a series of research initiatives, prototypes, and optimizations. We focused on adapting Stable Diffusion v2.1 for historical map generation.
Custom Dataset
We compiled and curated a 20+K dataset of “historical map – description” pairs, including the LIT source, which contains over 80,000 images. For experiments, datasets ranging from 700 to 8000 examples were used.
Model Training
The focus was on the U-Net component of the model architecture. We trained the model to better interpret textual prompts (e.g., “Europe in 1400” or “French borders in the 18th century”) and generate corresponding images with higher accuracy.
Process Enhancement
By leveraging memory-efficient optimizers, modifying network structures, and implementing aggressive training strategies, we reduced model training time by at least threefold.
Modular Approach
We developed a “maps-only” diffusion model concept—a network specializing exclusively in map generation. It allows eliminating distractions from other image types.
Geographic Detailing
Separate experiments were conducted to generate maps at the province, region, and smaller-unit levels, achieving high precision at micro-scales.
Stylistic Layer Design
We laid the architectural groundwork for future scalability—enabling maps to be generated with distinct thematic layers: borders, geography, culture, and languages.
Work Stages
Dataset Preparation
Basic Experiments with Stable Diffusion
Dataset Preparation
Manipulating Historical Events
Working with Long Texts
Training on Real Data
Process Optimization
Result Validation
Technologies
Business Value & Outcomes
High Accuracy
Generation of maps with correct borders and readable country names.
Flexibility
Ability to adjust to any historical period (1000–1800 AD) and region.
Scalability
Reduced training costs and ensured potential integration with GIS systems (OpenStreetMap).
Prototype Development
The solution serves as a foundation for further development of educational, analytical, or visualization tools.
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