TL;DR
- LGND raised $9M to develop AI tools for geospatial analysis
- Company aims to make querying Earth data as easy as ChatGPT
- Uses vector embeddings to simplify spatial computations
- Early use cases include wildfire break detection and vacation planning
- Targeting $400B+ geospatial data market
Earth Data, AI, and a $400B Opportunity
With Earth producing over 100 terabytes of satellite data per day, parsing this deluge of information has become increasingly challenging for governments, corporations, and researchers. One startup—LGND—is now taking on this data dilemma with a bold ambition: to become the ChatGPT for the Earth.
According to co-founder and CEO Nathaniel Manning, questions like “How many fire breaks does California have?” are deceptively complex. Traditionally, they required manual image reviews or single-use machine learning models costing hundreds of thousands of dollars. LGND wants to change that.
A Smarter Way to See the World
Backed by a $9 million seed round led by Javelin Venture Partners, with participation from AENU, Space Capital, and others, LGND is building tools that abstract complex geospatial analysis into vector embeddings—compact representations of spatial data that can power flexible queries.
Instead of retraining models for each specific question, LGND’s platform enables analysts to perform complex searches like:
“Show me all vegetation-free land segments wide enough to act as fire breaks in the last 12 months.”
This approach allows government agencies or private companies to answer dynamic, location-based questions with far less computing power and technical expertise.
What Are Geographic Embeddings?
LGND’s key innovation is geographic embeddings, a novel way to summarize location-based data. These embeddings condense 90% of the compute needed to classify and analyze features like rivers, roads, and even wildfire barriers—long before traditional models are even launched.
Use Case Example:
- Roads, rivers, and lakes may appear visually different in satellite data
- Embeddings cluster them together by functional similarity (i.e., acting as fire breaks)
- This allows for automated pattern detection across different terrains
According to co-founder and chief scientist Bruno Sánchez-Andrade Nuño, this method does not seek to replace analysts but to amplify their efficiency by 10x or even 100x.
Funding Details and Strategic Backers
| Investor | Notable Affiliation |
| Javelin Venture Partners | Lead investor |
| AENU | Climate VC |
| Space Capital | Geospatial-focused investor |
| John Hanke | Founder of Keyhole, precursor to Google Earth |
| Karim Atiyeh | Co-founder of Ramp |
| Suzanne DiBianca | Salesforce executive |
The round also included Coalition Operators, MCJ Collective, Ridgeline Ventures, and Clocktower Ventures.
From Wildfire Prevention to Vacation Planning
LGND’s core product is being developed in two formats:
- An enterprise app for non-technical users
- An API for developers who need granular control over geospatial data inputs
CEO Manning envisions applications beyond environmental monitoring. For instance, an AI travel agent could be asked:
“Find me a 3-bedroom rental on a white sand beach with low seaweed in February and no construction within 1km.”
Such multi-variable queries, while almost impossible to build with current tools, are feasible through LGND’s universal embeddings.
LGND’s Vision at a Glance
| Metric | Detail |
| Daily Earth Data | 100 TB (via satellites) |
| Target Market | $400B+ geospatial data economy |
| Seed Funding | $9M |
| Tech Stack | Geographic vector embeddings |
| Key Goal | Democratize spatial analysis for enterprises and analysts |
| Early Use Case | Fire break mapping, AI travel agents |
The Road Ahead for Geospatial AI
LGND believes its framework can do for Earth data what OpenAI’s models have done for language. As Manning puts it:
“We’re trying to be the Standard Oil for this data.”
Their platform could upend how insurance companies assess flood zones, how telecoms site 5G towers, and how governments track illegal deforestation—all without requiring PhDs in geoinformatics.
But success depends on scalability, ease of use, and enterprise adoption. If LGND can deliver, it may redefine how we query and interpret the planet itself.
