# Monitoring Urban Forests from Auto-Generated Segmentation Maps

arXiv: https://arxiv.org/abs/2206.06948 · HTML: https://mlsanity.com/paper/2206.06948/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 14 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [Weakly Supervised Semantic Segmentation of Satellite Images for Land Cover Mapping -- Challenges and Opportunities](https://mlsanity.com/paper/2002.08254/) (2020)
- [AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning](https://mlsanity.com/paper/2202.00067/) (2022)

## Built on by (most cited first, 4 of 4)

- [Aboveground carbon biomass estimate with Physics-informed deep network](https://mlsanity.com/paper/2210.13752/) (2022)
- [DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for Urban Climate Resilience](https://mlsanity.com/paper/2306.06269/) (2023)
- [AutoLCZ: Towards Automatized Local Climate Zone Mapping from Rule-Based Remote Sensing](https://mlsanity.com/paper/2405.13993/) (2024)
- [LandSegmenter: Towards a Flexible Foundation Model for Land Use and Land Cover Mapping](https://mlsanity.com/paper/2511.08156/) (2025)

## Abstract (arXiv)

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.