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arXiv Open Access 2022

Monitoring Urban Forests from Auto-Generated Segmentation Maps

Conrad M Albrecht Chenying Liu Yi Wang Levente Klein Xiao Xiang Zhu
Lihat Sumber

Abstrak

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.

Topik & Kata Kunci

cs.CV cs.AI cs.CY cs.LG

Penulis (5)

C

Conrad M Albrecht

C

Chenying Liu

Y

Yi Wang

L

Levente Klein

X

Xiao Xiang Zhu

Format Sitasi

APA MLA BibTeX
Albrecht, C.M., Liu, C., Wang, Y., Klein, L., Zhu, X.X. (2022). Monitoring Urban Forests from Auto-Generated Segmentation Maps. https://arxiv.org/abs/2206.06948

Akses Cepat

Lihat di Sumber
Informasi Jurnal
Tahun Terbit
2022
Bahasa
en
Sumber Database
arXiv
Akses
Open Access ✓