Idea
A global dataset and integrity scoring platform for reforestation projects enabling accurate location verification and satellite data access for environmental stakeholders
Research Paper
Core Innovation
This paper introduces the Location Data Integrity Score (LDIS), a novel metric to standardize and assess geographic boundary accuracy for reforestation sites globally. It compiles a large-scale dataset linking over 1.2 million planting sites with satellite imagery, improving data reliability and enabling better accountability in carbon offset markets. This approach addresses prior challenges of inconsistent location data and limited verification in voluntary environmental projects.
Market Size (TAM)
$2–10B TAM, $1–2B SAM; assumption: growing global carbon markets and increasing demand for verified environmental data.
Potential Customers & Pain Points
- Environmental NGOs needing reliable reforestation data
- Voluntary carbon market participants requiring verified project boundaries
- Satellite imagery analysts seeking labeled training data for vegetation monitoring
Business Model
Subscription-based API access to the dataset and LDIS scores for environmental organizations and carbon market participants; custom analytics services.
Competitive Landscape
- Global Forest Watch
- Planet Labs
- SilviaTerra
Implementation Challenges
- Data privacy and access restrictions
- Integration with existing carbon market platforms
- Ensuring continuous satellite data updates
Validation Strategy
- Pilot integration with select carbon offset projects
- User feedback from environmental NGOs on data usability
- Benchmarking LDIS against existing location verification methods
Research Paper Overview
A Global Dataset of Location Data Integrity-Assessed Reforestation Efforts
Summary
This study presents a comprehensive global dataset of afforestation and reforestation projects, covering over 1.2 million planting sites from 45,628 projects across 33 years. It introduces the Location Data Integrity Score (LDIS) to standardize and assess the accuracy of site-level geographic boundaries, addressing data reliability issues in voluntary carbon markets. The dataset also includes linked satellite imagery, enabling enhanced accountability and serving as valuable training data for computer vision applications.