TrueBranch: Metric Learning-based Verification of Forest Conservation Projects
This addresses the need for robust and low-cost monitoring in carbon offset programs to ensure trust and accuracy, representing an incremental improvement over existing automated methods by adding verification against untruthful reporting.
The paper tackles the problem of verifying the truthfulness of drone imagery reported by landowners in forest conservation projects to prevent fraud in carbon offset payments, proposing TrueBranch, a metric learning-based algorithm that matches drone imagery with satellite imagery, with preliminary results indicating that standard distance metrics are insufficient for reliable detection.
International stakeholders increasingly invest in offsetting carbon emissions, for example, via issuing Payments for Ecosystem Services (PES) to forest conservation projects. Issuing trusted payments requires a transparent monitoring, reporting, and verification (MRV) process of the ecosystem services (e.g., carbon stored in forests). The current MRV process, however, is either too expensive (on-ground inspection of forest) or inaccurate (satellite). Recent works propose low-cost and accurate MRV via automatically determining forest carbon from drone imagery, collected by the landowners. The automation of MRV, however, opens up the possibility that landowners report untruthful drone imagery. To be robust against untruthful reporting, we propose TrueBranch, a metric learning-based algorithm that verifies the truthfulness of drone imagery from forest conservation projects. TrueBranch aims to detect untruthfully reported drone imagery by matching it with public satellite imagery. Preliminary results suggest that nominal distance metrics are not sufficient to reliably detect untruthfully reported imagery. TrueBranch leverages metric learning to create a feature embedding in which truthfully and untruthfully collected imagery is easily distinguishable by distance thresholding.