# Machine Learning-based Estimation of Forest Carbon Stocks to increase Transparency of Forest Preservation Efforts

arXiv: https://arxiv.org/abs/1912.07850 · HTML: https://mlsanity.com/paper/1912.07850/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2019.

## Built on by (most cited first, 8 of 8)

- [GEO-Bench: Toward Foundation Models for Earth Monitoring](https://mlsanity.com/paper/2306.03831/) (2023)
- [Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark](https://mlsanity.com/paper/2112.00570/) (2021)
- [ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery](https://mlsanity.com/paper/2201.11192/) (2022)
- [Generating Physically-Consistent Satellite Imagery for Climate Visualizations](https://mlsanity.com/paper/2104.04785/) (2021)
- [Quantification of Carbon Sequestration in Urban Forests](https://mlsanity.com/paper/2106.00182/) (2021)
- [Tackling the Overestimation of Forest Carbon with Deep Learning and Aerial Imagery](https://mlsanity.com/paper/2107.11320/) (2021)
- [Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process](https://mlsanity.com/paper/2411.10074/) (2024)
- [Environment-Aware Satellite Image Generation with Diffusion Models](https://mlsanity.com/paper/2509.24875/) (2025)

## Abstract (arXiv)

An increasing amount of companies and cities plan to become CO2-neutral, which requires them to invest in renewable energies and carbon emission offsetting solutions. One of the cheapest carbon offsetting solutions is preventing deforestation in developing nations, a major contributor in global greenhouse gas emissions. However, forest preservation projects historically display an issue of trust and transparency, which drives companies to invest in transparent, but expensive air carbon capture facilities. Preservation projects could conduct accurate forest inventories (tree diameter, species, height etc.) to transparently estimate the biomass and amount of stored carbon. However, current rainforest inventories are too inaccurate, because they are often based on a few expensive ground-based samples and/or low-resolution satellite imagery. LiDAR-based solutions, used in US forests, are accurate, but cost-prohibitive, and hardly-accessible in the Amazon rainforest. We propose accurate and cheap forest inventory analyses through Deep Learning-based processing of drone imagery. The more transparent estimation of stored carbon will create higher transparency towards clients and thereby increase trust and investment into forest preservation projects.