This content originally appeared on DEV Community and was authored by SHIFA NOORULAIN
Geolocating History with AI: How Large Language Models are Mapping Colonial Virginia Land Grants
Ever wondered how AI can uncover historical secrets? We're using Large Language Models (LLMs) for geolocating Colonial Virginia land grants.
TL;DR
LLMs can extract location data from historical text.
This data can be used to create interactive historical maps.
Overcoming challenges like ambiguous language is key.
The method can be adapted to other historical datasets.
It opens new doors for historical research and education.
Background (Only what’s needed)
Imagine trying to map old property records from centuries ago. The descriptions are often vague and use landmarks that no longer exist. This is a real problem faced by historians.
Large Language Models (LLMs) are AI models trained on vast amounts of text. They understand language nuances and can extract specific information. Think of it like teaching a computer to read old documents and find the important details. One application is geolocating historical land grants.
Historical land grants often contain textual descriptions of land boundaries. Extracting locations from these descriptions manually is time-consuming. LLMs can automate this process, making it faster and more efficient. Want to see this in action? Jump to Mini Project.
You can read more about LLMs here: https://example.com/docs.
Using LLMs for Geolocating Land Grants
LLMs can process the textual descriptions of land grants. They can identify phrases indicating location. These locations are often in relation to rivers, hills, and other natural landmarks.
The LLM is trained to identify these landmarks. It then uses them to infer the approximate geographical coordinates. This process lets us map the land grants.
Here's a simplified workflow:
Data Input: The LLM receives the text of a land grant.
Named Entity Recognition (NER): The LLM identifies place names, landmarks, and people.
Relationship Extraction: The LLM determines the relationships between these entities (e.g., "north of the river").
Geocoding: The LLM converts the textual description into geographical coordinates.
Map Visualization: The coordinates are plotted on a map.
![diagram: end-to-end flow of Geolocating History with AI: How Large Language Models are Mapping Colonial Virginia Land Grants]
Here’s a minimal Python code example using a hypothetical LLM library.
This is a placeholder - replace with actual LLM API usage
def geolocate_land_grant(text):
"""
Geolocates a land grant description using an LLM.
Args:
text: The textual description of the land grant.
Returns:
A tuple containing the latitude and longitude, or None if not found.
"""
# Simulate LLM processing
if "river" in text.lower() and "hill" in text.lower():
return (37.5, -77.4) # Example coordinates
else:
return None
land_grant_text = "Land north of the James River near a prominent hill."
coordinates = geolocate_land_grant(land_grant_text)
if coordinates:
print(f"Geolocated coordinates: {coordinates}")
else:
print("Could not geolocate the land grant.")
Your Mini-Checklist:
Understand the LLM workflow.
Explore available LLM libraries.
Adapt the workflow to your dataset.
Challenges and Solutions in Geolocating
Geolocating using LLMs is not always straightforward. We face challenges like:
Ambiguity: Old descriptions can be vague and use terms that are no longer in use.
Data Scarcity: Georeferenced historical maps for training LLMs are scarce.
Computational Cost: Training and running LLMs can be computationally expensive. This is especially relevant in India where bandwidth is a concern.
Here are some solutions to overcome these challenges:
Fine-tuning: Fine-tune the LLM on a specific dataset of historical land grants.
Data Augmentation: Use data augmentation techniques to increase the size of the training dataset.
Transfer Learning: Use transfer learning to leverage pre-trained LLMs and reduce training costs. Consider models optimized for lower bandwidth.
Contextual Analysis: Use contextual analysis to disambiguate ambiguous descriptions.
![image: high-level architecture overview]
Common Pitfalls & How to Avoid
Over-reliance on LLMs: LLMs are powerful, but not perfect. Always verify the results with other sources.
Ignoring data quality: Garbage in, garbage out. Clean and preprocess your data carefully.
Insufficient training data: The LLM needs enough data to learn the patterns in the historical text.
Not accounting for coordinate systems: Ensure that the coordinates are in the correct coordinate system.
Lack of domain expertise: Consult with historians and geographers to ensure the accuracy of your results.
Assuming all land grants are accurate: Historical records can contain errors. Be aware of this and account for it in your analysis.
Mini Project — Try It Now
Let's try a simplified example using a pre-trained LLM. This assumes you have access to an LLM API (e.g., OpenAI, Cohere).
Install the necessary libraries:
Replace with your LLM provider's library (e.g., openai, cohere)
pip install transformers
Import the libraries:
from transformers import pipeline
Load a pre-trained model:
Choose a suitable LLM (e.g., "bert-base-uncased")
ner_pipe = pipeline("ner", model="dslim/bert-base-NER") #Named Entity Recognition
Define the land grant text:
land_grant_text = "Land located 5 miles north of Jamestown, near the Chickahominy River."
Extract location entities:
entities = ner_pipe(land_grant_text)
print(entities) #Examine the found location entities.
Process the extracted entities: You'll need to write further code to convert the entities into coordinates. This often requires geocoding APIs (e.g., Google Maps API).
Visualize the results: Plot the coordinates on a map using a library like folium.
Important: This example requires an LLM API key and further geocoding and mapping steps. It’s a simplified starting point.
Key Takeaways
LLMs automate extraction of locations from historical texts.
Challenges include vague language & compute costs.
Consider domain experts and data quality.
Iterate to improve your LLM model.
"Location is everything" – even in historical data.
CTA
Try running the mini project with a free LLM API trial and share your findings with the developer community! Let’s build a geo-located history together.
This content originally appeared on DEV Community and was authored by SHIFA NOORULAIN
SHIFA NOORULAIN | Sciencx (2025-10-06T14:58:03+00:00) Geolocating History with AI: How Large Language Models are Mapping Colonial Virginia Land Grants. Retrieved from https://www.scien.cx/2025/10/06/geolocating-history-with-ai-how-large-language-models-are-mapping-colonial-virginia-land-grants/
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