Extraction of Urban Vegetation in Highly Dense Urban Environment with Application to Measure Inhabitants’ Satisfaction of Urban Green Space

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ISSN Print: 2151-1950
ISSN Online: 2151-1969
www.scirp.org/journal/jgis
E-mail: jgis@scirp.org

"Extraction of Urban Vegetation in Highly Dense Urban Environment with Application to Measure Inhabitants’ Satisfaction of Urban Green Space"
written by Fatwa Ramdani,
published by Journal of Geographic Information System, Vol.5 No.2, 2013

has been cited by the following article(s):

[1]	The simplicity of XGBoost algorithm versus the complexity of Random Forest, Support Vector Machine, and Neural Networks algorithms in urban forest …
	F1000Research, 2022

[2]	Implementation and Evaluation of Urban Green Space Supply for Debre Berhan Town, Ethiopia
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[3]	MONITORING LAHAN TERBANGUN DI KABUPATEN BOGOR DENGAN CITRA LANDSAT 8 MENGGUNAKAN METODE TASSELED CAP TRANSFORMATION (TCT)
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[4]	Combination of supervised and unsupervised classifiers based on belief functions
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[5]	Environmental quality evaluation in Dhaka City Corporation–using satellite imagery
	2019

[6]	Analysis of the application of an advanced classifier algorithm to ultra-high resolution unmanned aerial aircraft imagery–a neural network approach
	2019

[7]	Application of TCT as a Remote Sensing Change Detection Technique: A Temporal Case Study of Lahore District–Pakistan
	2017

[8]	Urban vegetation mapping from fused hyperspectral image and LiDAR data with application to monitor urban tree heights
	Journal of Geographic Information System, 2013

[1]	The simplicity of XGBoost algorithm versus the complexity of Random Forest, Support Vector Machine, and Neural Networks algorithms in urban forest classification F1000Research, 2022 DOI:10.12688/f1000research.124604.1

[2]	The simplicity of XGBoost algorithm versus the complexity of Random Forest, Support Vector Machine, and Neural Networks algorithms in urban forest classification F1000Research, 2022 DOI:10.12688/f1000research.124604.1

[3]	The simplicity of XGBoost algorithm versus the complexity of Random Forest, Support Vector Machine, and Neural Networks algorithms in urban forest classification F1000Research, 2022 DOI:10.12688/f1000research.124604.1

[4]	Analysis of the application of an advanced classifier algorithm to ultra-high resolution unmanned aerial aircraft imagery – a neural network approach International Journal of Remote Sensing, 2020 DOI:10.1080/01431161.2019.1688413

[5]	Analysis of the application of an advanced classifier algorithm to ultra-high resolution unmanned aerial aircraft imagery – a neural network approach International Journal of Remote Sensing, 2019 DOI:10.1080/01431161.2019.1688413

[6]	Urban Vegetation Mapping from Fused Hyperspectral Image and LiDAR Data with Application to Monitor Urban Tree Heights Journal of Geographic Information System, 2013 DOI:10.4236/jgis.2013.54038

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