# Quantification of Carbon Sequestration in Urban Forests

arXiv: https://arxiv.org/abs/2106.00182 · HTML: https://mlsanity.com/paper/2106.00182/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Machine Learning-based Estimation of Forest Carbon Stocks to increase Transparency of Forest Preservation Efforts](https://mlsanity.com/paper/1912.07850/) (2019)
- [Monitoring the Impact of Wildfires on Tree Species with Deep Learning](https://mlsanity.com/paper/2011.02514/) (2020)
- [PAIRS AutoGeo: an Automated Machine Learning Framework for Massive Geospatial Data](https://mlsanity.com/paper/2012.06907/) (2020)

## Built on by (most cited first, 2 of 2)

- [AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning](https://mlsanity.com/paper/2202.00067/) (2022)
- [Aboveground carbon biomass estimate with Physics-informed deep network](https://mlsanity.com/paper/2210.13752/) (2022)

## Abstract (arXiv)

Vegetation, trees in particular, sequester carbon by absorbing carbon dioxide from the atmosphere. However, the lack of efficient quantification methods of carbon stored in trees renders it difficult to track the process. We present an approach to estimate the carbon storage in trees based on fusing multi-spectral aerial imagery and LiDAR data to identify tree coverage, geometric shape, and tree species -- key attributes to carbon storage quantification. We demonstrate that tree species information and their three-dimensional geometric shapes can be estimated from aerial imagery in order to determine the tree's biomass. Specifically, we estimate a total of 52,000 tons of carbon sequestered in trees for New York City's borough Manhattan.