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LGND AI Wins Snowflake Startup Challenge

LGND AI wins the 2026 Snowflake Startup Challenge with its Large Earth Models, making global imagery data accessible for AI.

6 min read
LGND AI team celebrating winning the Snowflake Startup Challenge
LGND AI takes home the top prize at the 2026 Snowflake Startup Challenge.· Snowflake

LGND AI has clinched the top spot in the 2026 Snowflake Startup Challenge. The company's innovative approach to Earth observation data stood out to judges looking for groundbreaking technology and strong team dynamics.

Visual TL;DR. Earth Imagery Data trained on LGND AI's LEMs. LGND AI's LEMs enables Query Physical World. LGND AI's LEMs uses Snowflake Platform. Snowflake Platform supports Make Planet Queryable. Query Physical World led to Wins Snowflake Challenge. Use Cases showcased Wins Snowflake Challenge.

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  1. Earth Imagery Data: vast amounts of planetary imagery data, 800 petabytes
  2. LGND AI's LEMs: Large Earth Models trained on planetary imagery
  3. Query Physical World: answer queries about physical world events directly from visual data
  4. Snowflake Platform: leverages Snowflake's platform for processing diverse data modalities
  5. Make Planet Queryable: ambitious goal of making the entire planet queryable through imagery
  6. Wins Snowflake Challenge: clinched the top spot in the 2026 Snowflake Startup Challenge
  7. Use Cases: demonstrated applicability in insurance, climate risk, and government
Visual TL;DR
Visual TL;DR — startuphub.ai Earth Imagery Data trained on LGND AI's LEMs. LGND AI's LEMs enables Query Physical World. Query Physical World led to Wins Snowflake Challenge trained on enables led to Earth Imagery Data LGND AI's LEMs Query Physical World Wins Snowflake Challenge From startuphub.ai · The publishers behind this format
Visual TL;DR — startuphub.ai Earth Imagery Data trained on LGND AI's LEMs. LGND AI's LEMs enables Query Physical World. Query Physical World led to Wins Snowflake Challenge trained on enables led to Earth ImageryData LGND AI's LEMs Query PhysicalWorld Wins SnowflakeChallenge From startuphub.ai · The publishers behind this format
Visual TL;DR — startuphub.ai Earth Imagery Data trained on LGND AI's LEMs. LGND AI's LEMs enables Query Physical World. Query Physical World led to Wins Snowflake Challenge trained on enables led to Earth Imagery Data vast amounts of planetary imagery data,800 petabytes LGND AI's LEMs Large Earth Models trained on planetaryimagery Query Physical World answer queries about physical world eventsdirectly from visual data Wins Snowflake Challenge clinched the top spot in the 2026Snowflake Startup Challenge From startuphub.ai · The publishers behind this format
Visual TL;DR — startuphub.ai Earth Imagery Data trained on LGND AI's LEMs. LGND AI's LEMs enables Query Physical World. Query Physical World led to Wins Snowflake Challenge trained on enables led to Earth ImageryData vast amounts ofplanetary imagerydata, 800 petabytes LGND AI's LEMs Large Earth Modelstrained onplanetary imagery Query PhysicalWorld answer queriesabout physicalworld events… Wins SnowflakeChallenge clinched the topspot in the 2026Snowflake Startup… From startuphub.ai · The publishers behind this format
Visual TL;DR — startuphub.ai Earth Imagery Data trained on LGND AI's LEMs. LGND AI's LEMs enables Query Physical World. LGND AI's LEMs uses Snowflake Platform. Snowflake Platform supports Make Planet Queryable. Query Physical World led to Wins Snowflake Challenge. Use Cases showcased Wins Snowflake Challenge trained on enables uses supports led to showcased Earth Imagery Data vast amounts of planetary imagery data,800 petabytes LGND AI's LEMs Large Earth Models trained on planetaryimagery Query Physical World answer queries about physical world eventsdirectly from visual data Snowflake Platform leverages Snowflake's platform forprocessing diverse data modalities Make Planet Queryable ambitious goal of making the entire planetqueryable through imagery Wins Snowflake Challenge clinched the top spot in the 2026Snowflake Startup Challenge Use Cases demonstrated applicability in insurance,climate risk, and government From startuphub.ai · The publishers behind this format
Visual TL;DR — startuphub.ai Earth Imagery Data trained on LGND AI's LEMs. LGND AI's LEMs enables Query Physical World. LGND AI's LEMs uses Snowflake Platform. Snowflake Platform supports Make Planet Queryable. Query Physical World led to Wins Snowflake Challenge. Use Cases showcased Wins Snowflake Challenge trained on enables uses supports led to showcased Earth ImageryData vast amounts ofplanetary imagerydata, 800 petabytes LGND AI's LEMs Large Earth Modelstrained onplanetary imagery Query PhysicalWorld answer queriesabout physicalworld events… SnowflakePlatform leveragesSnowflake'splatform for… Make PlanetQueryable ambitious goal ofmaking the entireplanet queryable… Wins SnowflakeChallenge clinched the topspot in the 2026Snowflake Startup… Use Cases demonstratedapplicability ininsurance, climate… From startuphub.ai · The publishers behind this format

The core of LGND AI's offering is its development of Large Earth Models (LEMs). These models are trained on an immense 800 petabytes of planetary imagery, a stark contrast to the language-based training of traditional Large Language Models (LLMs). This allows LGND AI to answer queries about physical world events, such as deforestation in the Amazon, directly from visual data.

By leveraging Snowflake's platform, LGND AI can process diverse imagery and data modalities at scale. This integration provides operational flexibility and supports their ambitious goal of making the entire planet queryable through imagery.

Making Earth Imagery Actionable

LGND AI demonstrated the widespread applicability of its technology through use cases in insurance and climate risk, government intelligence, and AI agents. The ability to analyze geospatial imagery opens new avenues for business decisions, from assessing wildfire risks to optimizing travel plans by filtering out areas with active construction.

"We believe that in the not-too-distant future, our largest user bases won't be just humans — it will be agents, robots and other AI models," stated Nathaniel Manning, CEO of LGND AI. "Every AI that wants to understand the physical world is going to need what we're building."

Snowflake judges praised LGND AI's ambition and the potential impact of its mission. Benoit Dageville, Co-Founder and President of Product at Snowflake, highlighted the broad theme of understanding Earth as a whole, while CMO Denise Persson emphasized the company's focus on solving significant problems for Earth and humanity.

Runners-up Airrived and Twine Security were also recognized. Airrived presented its Agentic OS for autonomous AI agents, and Twine Security showcased its AI digital employees built with Snowflake Cortex AI for identity and access management.

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#LGND AI#Snowflake#Startup Challenge#AI#Geospatial AI#Large Earth Models#Data Cloud