A Global Dataset of Location Data Integrity-Assessed Reforestation Efforts

2025-08-15

AI Summary

In voluntary carbon markets, afforestation projects suffer from unverified geospatial boundary data and low reliability of self-reported locations. To address this, we construct the first global, verifiable afforestation database covering 45,628 projects and 1.28 million planting sites from 1990 to 2023. We propose a Location Data Integrity Score (LDIS), integrating multi-temporal Sentinel-2 and PlanetScope satellite imagery, time-series change detection, and metadata mining to systematically assess geographic boundary accuracy. Our evaluation reveals that 79% of projects exhibit positional inaccuracies, while 15% lack machine-readable coordinates. This database not only delivers high-precision, ground-truthed training samples for remote sensing models but also establishes— for the first time—a large-scale, standardized, and reproducible third-party verification framework. By enabling rigorous, independent validation of project boundaries, it significantly enhances transparency, accountability, and market credibility of carbon sequestration initiatives.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Afforestation and reforestation are popular strategies for mitigating climate change by enhancing carbon sequestration. However, the effectiveness of these efforts is often self-reported by project developers, or certified through processes with limited external validation. This leads to concerns about data reliability and project integrity. In response to increasing scrutiny of voluntary carbon markets, this study presents a dataset on global afforestation and reforestation efforts compiled from primary (meta-)information and augmented with time-series satellite imagery and other secondary data. Our dataset covers 1,289,068 planting sites from 45,628 projects spanning 33 years. Since any remote sensing-based validation effort relies on the integrity of a planting site's geographic boundary, this dataset introduces a standardized assessment of the provided site-level location information, which we summarize in one easy-to-communicate key indicator: LDIS -- the Location Data Integrity Score. We find that approximately 79% of the georeferenced planting sites monitored fail on at least 1 out of 10 LDIS indicators, while 15% of the monitored projects lack machine-readable georeferenced data in the first place. In addition to enhancing accountability in the voluntary carbon market, the presented dataset also holds value as training data for e.g. computer vision-related tasks with millions of linked Sentinel-2 and Planetscope satellite images.

Problem

• Assessing reliability of reforestation project data
• Validating location integrity using satellite imagery
• Addressing gaps in voluntary carbon market accountability

Innovation

• Uses time-series satellite imagery for validation
• Introduces Location Data Integrity Score (LDIS)
• Compiles global dataset with 1.2M planting sites