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Coolant

Coolant provides AI-powered solutions for precise forest monitoring and carbon measurement. The platform leverages advanced machine learning and geospatial data to deliver accurate ecological assessments. This enables clients to reliably track forest health and quantify carbon sequestration efforts.

Cambridge, United KingdomFounded 20234300+ followers
Updated 20 months ago

Funding

$800K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.

Funding rounds are not available yet.

Founders

Product

Problem

Traditional forestry monitoring methods, such as LiDAR, are often expensive and complex, hindering accurate and frequent assessments of reforestation projects. This limits the ability to precisely measure tree carbon stock and track individual tree metrics, impacting the effectiveness of carbon offset programs.

Solution

Coolant offers a cost-effective solution for reforestation monitoring by leveraging aerial drone footage and advanced computer vision techniques. The platform creates accurate 3D models of reforestation sites, achieving over 95% accuracy compared to LiDAR in measuring tree carbon stock. By using consumer-grade drones like the DJI Mini and Mavic 2, Coolant transforms drone footage into hyper-realistic 3D models, enabling precise assessments of individual tree metrics such as height and crown width. This technology provides detailed insights into forest health and carbon sequestration, enhancing the transparency and reliability of carbon offset projects.

Target Audience

The primary customers are organizations involved in reforestation projects, forestry management, and carbon offset programs seeking accurate and cost-effective monitoring solutions.

Features

  • Generates 3D models of reforestation sites from aerial drone footage.
  • Employs computer vision techniques for accurate depth mapping of forests.
  • Measures individual tree metrics, including height and crown width.
  • Achieves over 95% accuracy compared to airborne LiDAR for measuring tree carbon stock.
  • Compatible with consumer-grade drones like the DJI Mini and Mavic 2.
This profile is AI-generated and may contain inaccuracies.