Unlike traditional carbon credits that rely on manual audits performed once every few years, CarboCredit utilizes Machine Learning and Computer Vision to monitor environmental projects in real-time.
AI models analyze multi-spectral satellite imagery to track reforestation progress and biomass density with unprecedented accuracy.
The system predicts future carbon sequestration based on local climate data, soil health, and historical growth patterns.
For renewable energy projects, AI ingests data directly from smart meters and IoT sensors to verify energy output without human intervention.
The global voluntary carbon market has historically suffered from double-counting, slow manual verification, and a lack of transparency. Our AI changes everything.
A multi-layered approach to environmental verification
Our system continuously ingests data from multiple sources: satellite imagery, IoT sensors, weather stations, and smart meters. Over 50TB of environmental data processed daily.
Advanced neural networks analyze satellite imagery to detect changes in forest cover, vegetation health, and land use. Our models can identify individual tree growth with 94% accuracy.
ML algorithms cross-reference visual data with historical patterns, climate models, and ground-truth data to calculate precise carbon sequestration values.
Results are verified against our proprietary trust score algorithm and immediately published to the blockchain, triggering smart contract payouts when thresholds are met.
See how AI-powered verification is transforming the carbon credit industry.