Individual tree detection in large-scale urban environments using high-resolution multispectral imagery-Reference-Cited by-同舟云学术

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Individual tree detection in large-scale urban environments using high-resolution multispectral imagery

Published:2024-06 Issue: Volume:130 Page:103848
ISSN:1569-8432
Container-title:International Journal of Applied Earth Observation and Geoinformation
language:en
Short-container-title:International Journal of Applied Earth Observation and Geoinformation

Author:

Ventura Jonathan^ORCID,Pawlak Camille,Honsberger Milo,Gonsalves Cameron,Rice Julian^ORCID,Love Natalie L.R.,Han Skyler^ORCID,Nguyen Viet,Sugano Keilana,Doremus Jacqueline,Fricker G. Andrew,Yost Jenn,Ritter Matt

Funder

U.S. Forest Service

California State Polytechnic University, Pomona

California Department of Forestry and Fire Protection

Publisher

Elsevier BV

Reference63 articles.

1. TensorFlow: A system for large-scale machine learning;Abadi,2016

2. Optuna: A next-generation hyperparameter optimization framework;Akiba,2019

3. Identifying Santa Barbara’s urban tree species from AVIRIS imagery using canonical discriminant analysis;Alonzo;Remote Sens. Lett.,2013

4. The use of three-dimensional convolutional neural networks to interpret LiDAR for forest inventory;Ayrey;Remote Sens.,2018

5. Accurate delineation of individual tree crowns in tropical forests from aerial RGB imagery using Mask R-CNN;Ball;Remote Sens. Ecol. Conserv.,2023

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Earth Observation and Machine Learning for Climate Change;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

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