Airborne data and machine learning for urban tree species mapping: Enhancing the legend design to improve the map applicability for city greenery management-Reference-Cited by-同舟云学术

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Airborne data and machine learning for urban tree species mapping: Enhancing the legend design to improve the map applicability for city greenery management

Published:2024-04 Issue: Volume:128 Page:103719
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:

Niedzielko Jan^ORCID,Kopeć Dominik^ORCID,Wylazłowska Justyna,Kania Adam,Charyton Jakub,Halladin-Dąbrowska Anna^ORCID,Niedzielko Maria,Berłowski Karol

Publisher

Elsevier BV

Reference72 articles.

1. Automatic Tree Species Recognition with Quantitative Structure Models;Åkerblom;Remote Sens. Environ.,2017

2. Urban Tree Species Mapping Using Hyperspectral and LiDAR Data Fusion;Alonzo;Remote Sens. Environ.,2014

3. ATCOR4 Manual. ReSe Applications. Available online: https://www.rese-apps.com/pdf/atcor4_manual.pdf (accessed on 23 September 2022).

4. Use of Watersheds in Contour Detection;Beucher,1979

5. Random Forests;Breiman;Mach. Learn.,2001

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