# Monitoring Urban Forests from Auto-Generated Segmentation Maps

Source: [Monitoring Urban Forests from Auto-Generated Segmentation Maps](<https://www.sota2.com/research/paper/68fa6cc81e209131cea35f51>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:18:38.162Z

Authors: Conrad M Albrecht, Chenying Liu, Yi Wang, Levente Klein, Xiao Xiang Zhu

Published: 2022-06-14T16:06:58.000Z

## Abstract

We present and evaluate a weakly-supervised methodology to quantify the spatio-temporal distribution of urban forests based on remotely sensed data with close-to-zero human interaction. Successfully training machine learning models for semantic segmentation typically depends on the availability of high-quality labels. We evaluate the benefit of high-resolution, three-dimensional point cloud data (LiDAR) as source of noisy labels in order to train models for the localization of trees in orthophotos. As proof of concept we sense Hurricane Sandy's impact on urban forests in Coney Island, New York City (NYC) and reference it to less impacted urban space in Brooklyn, NYC.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2206.06948>)

[PDF](<https://arxiv.org/pdf/2206.06948.pdf>)