EnviDa Protocol
  • 🌍About Envida Blockchain
    • Introduction
    • Overview
    • Why EnviDa?
    • How it works?
    • Understanding the EnviDa Blockchain
    • DeFi Essentials
    • The Joint Potential of Blockchain, IOT and AI
  • πŸš™What is DriveMiner?
    • Environmental Tracking Devices
    • The Cost of Mining
    • The Solution of Mining
    • POW Consensus
    • Technical Specs
    • Device Sales and ROI
  • πŸ’°$EDAT Tokenomics
    • Detailed EDAT Tokenomics
    • ICO and Token Listings
    • Token Unlock and Allocation
    • Game Theory
  • πŸ“ˆStaking and Rewards
    • Reward Distribution
    • DriveMining Rewards
    • Validator Rewards
      • Unstaking Penalty
    • Staking Pool Rewards
      • Discounts
    • Data Collection Rewards
      • Stream Payment - Sensors
        • Competition and Arrival
      • Stream Payment - Lidiar
        • Teamwork
      • Pool Bonus
      • Formulas
  • ROI
  • πŸ¦Έβ€β™‚οΈFor Users
    • EnviDa Wallet
    • Driver Experience
    • Data Marketplace
    • DeFi Essentials
    • External Wallets
  • ⛓️Blockchain Architecture
    • Trustless EVM
    • Bridges
    • Runtime Development
    • Cross-Chain Compatibility
    • EnviDa Dex Engine
    • Technical Architecture
    • Scalability
    • Indexer & Explorer
    • Application Security
    • Storage
    • IPLD
    • Governance
    • Liquidity
    • Concensus
    • Runtime Environment
  • πŸ“”API Recipes
    • Account
    • Asset
    • Bag
    • Bundle
    • Market
  • 🌐EnviDa Network Infrastructure
  • ☸️Nodes and Validators
  • 🧠AI & Simulations
  • 🀝Network Referral System
  • πŸ›£οΈRoadmap
  • πŸ“šResources
    • Website
    • Business Whitepaper
    • Github
    • Technical Whitepaper
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  1. About Envida Blockchain

Why EnviDa?

Environmental tracking and lidar scanning using generative AI involves the use of AI algorithms to analyze data collected from lidar (Light Detection and Ranging) sensors, which use lasers to measure distances and create detailed 3D maps of the environment. The AI algorithms can be trained on large datasets to generate predictive models of environmental conditions, such as vegetation cover, topography, and soil moisture, allowing for more accurate and efficient tracking of environmental changes over time.

Generative AI can also be used to improve the accuracy and resolution of lidar scans by generating synthetic data that can be used to train and validate the algorithms. This can help to fill in gaps in the data and reduce the amount of manual labor required for data collection and analysis.

Overall, the use of generative AI for environmental tracking and lidar scanning can provide more accurate and comprehensive insights into the state of the environment, enabling better decision making and management of natural resources.

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Last updated 2 years ago

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