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ForestBench: Equitable Benchmarks for Monitoring, Reporting, and Verification of Nature-Based Solutions with Machine Learning
Lucas Czech · Björn Lütjens · David Dao
Event URL: https://www.climatechange.ai/papers/neurips2022/107 »

Restoring ecosystems and reducing deforestation are necessary tools to mitigate the anthropogenic climate crisis. Current measurements of forest carbon stock can be inaccurate, in particular for underrepresented and small-scale forests in the Global South, hindering transparency and accountability in the Monitoring, Reporting, and Verification (MRV) of these ecosystems. There is thus need for high quality datasets to properly validate ML-based solutions. To this end, we present ForestBench, which aims to collect and curate geographically-balanced gold-standard datasets of small-scale forest plots in the Global South, by collecting ground-level measurements and visual drone imagery of individual trees. These equitable validation datasets for ML-based MRV of nature-based solutions shall enable assessing the progress of ML models for estimating above-ground biomass, ground cover, and tree species diversity.

Author Information

Lucas Czech (Carnegie Institution for Science)
Björn Lütjens (Massachusetts Institute of Technology)
David Dao (ETH Zurich)

David Dao is a PhD student at ETH Zurich and the founder of GainForest, a non-profit working on decentralized technology to prevent deforestation. His research focuses on the deployment of novel machine learning systems for sustainable development and ecosystem monitoring. David served as a workshop co-organizer at ICLR, ICML and NeurIPS, and is a core member at Climate Change AI, a Global Shaper at World Economic Forum and a Climate Leader at Climate Reality. He is a research intern with Microsoft and was a former researcher at UC Berkeley and Stanford University.

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