An Open Network for the World's Geospatial Analytics

The Flyingcarpet network provides organizations and smart contracts with geospaital truth. Data scientists compete to create machine learning models from satellite imagery. Models are tradable via NFT ownership tokens.


Aerial Data as a Source of Truth

Models are created for new geospatial use cases via a decentralized competition of data scientists. Competition funding is incentivised via model ownership NFTs that encapsulate all future model revenue. As smart contract oracles, these models enable game-changing decentralized applications, such as parametric insurance where pre- and post-state satellite analytics can trigger automated claims.

From DAOs, to insurance companies, to agri-companies, to governments, the Flyingcarpet network enables actionable insights through rich AI-powered analytics.

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Coconut Fields

Namaliu, a farmer in Papua New Guinea, has a problem. He wastes countless days walking up and down his coconut plantation with a clipboard and pen, manually counting his yield and inspecting his trees. The machine learning model instead processes visual data of Namaliu's plantation, extracting insights and analytics about its projected harvest.

Now, not only can he track theft and monitor yield much more effectively, he can also sell the analytics to third parties, such as government bodies or commodity traders.

Digitising Infrastructure

Infrastructure projects are incredibly costly, typically involve multiple stakeholders, and can be dangerous for the feet on the ground. A machine learning model can process the visual data relating to the bridge and extract insights and analytics about its condition.

Any abnormalities or weak sections are identified and rectified before they become costly - or deadly - problems. Flyingcarpet models can analyse a huge range of infrastructure, including rooftops, roads and power lines.

Setting Insurance on Fire

Insurance claims often take years to settle, payments are based on generic historical data, and brokers typically take a hefty 25% commission. With Flyingcarpet, a machine learning model determines the physical state of insured locations by processing raw satellite imagery gathered both before and after natural disasters occur.

By building on top of the decentralized Ethereum blockchain, insurers and customers operate within a commission-less protocol, enabling participants to capture all network value.

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    Raw imagery is sourced primarily from satellites. However, plane and drone data is also used for specific use cases.

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    Unleash the collaborative power of decentralised data scientists. Machine learning competitions incentivise participants to create cutting-edge models that extract analytics and insights for specific use cases.

    • Safety

    • Security

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    Ownership NFT tokens are minted for successful models. With no middleman fees, ownership NFT tokens capture all model value for network participants.


  • Julien Bouteloup
  • Leopold Joy
    Lead Developer
  • Evan Shay
    Sales and Marketing
  • Dan Roberts
    Operations Lead
  • Karel Dukota
    PhD Artificial Intelligence
  • Victor Faramond
  • Savannah Lee
    Public Relations
  • Samantha Yap
    Public Relations
  • Satya Doraisamy
    Legal Lead
  • Max Polwin
    Community Manager


  • Jane Thomason
    Digital Transformation Abt Associates
  • Viktor Tron
    Ethereum Foundation Developer. SWARM


Supported By

  • Q3 - 2017 - First Proof of Concept Successfully Completed

    An analytics-extraction algorithm running on drone imagery counted the number of coconuts in a plantation, equipping the farmer with relevant analytics and opening up significant supplemental income channels for the farmer.

  • Q1 - 2018 - Team Growth

    From a drone developer to a performance marketer our list of contributors grows to 11 and counting.

  • Q2 - 2018 - Winners of CogX London!

    Best in Category for Blockchain solutions for IoT.

  • Q3 - 2018 - Whitepaper Published

    Read the latest Flyingcarpet paper here.

  • Q3 - 2018 - Partners Announced

    From industry to technology partners, we'll be announcing our key strategic alliances created to boost the network in its early days.

  • Q3 - 2018 - Testnet Alpha Launch

    Testnet launched to showcase addition and staking of geospatial analytics opportunities.

  • Q4 - 2018 - Testnet Beta Launch

    This iteration of the testnet will include the dataset annotation portal and initial machine learning model integrations.

  • '19 - Mainnet

    Fully operational geospatial services network.

  • Q3 - 2017

    First proof of concept successfully completed

  • Q1 - 2018

    Team growth

  • Q2 - 2018

    Winners of Cogx London!

  • Q3 - 2018

    Whitepaper published

  • Q3 - 2018

    Technology and industry partners announced

  • Q3 - 2018

    Testnet alpha launch

  • Q4 - 2018

    Testnet beta launch

  • '19

    Mainnet launch