Monitoring Urban Forests from Auto-Generated Segmentation Maps
Conrad M Albrecht, Chenying Liu, Yi Wang, Levente Klein, Xiao Xiang, Zhu

TL;DR
This paper introduces a weakly-supervised approach to monitor urban forests using remotely sensed data, leveraging LiDAR-generated noisy labels to train segmentation models with minimal human input, demonstrated through Hurricane Sandy's impact assessment in NYC.
Contribution
It proposes a novel weakly-supervised methodology utilizing LiDAR data as noisy labels for urban forest segmentation from orthophotos, reducing the need for manual annotations.
Findings
Effective localization of urban trees using noisy LiDAR labels
Successful assessment of hurricane impact on NYC urban forests
Minimal human interaction required for model training
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.
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Taxonomy
TopicsRemote Sensing and LiDAR Applications · Remote Sensing in Agriculture · Land Use and Ecosystem Services
