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Machine Learning for Modeling Vegetation Restoration of For..:

, In: 2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI),

Karimi, Saeideh ; Heidari, Mehdi ; Mosavi, Amir - p. 000531-000538 , 2024

Link: https://doi.org/10.1109/SAMI60510.2024.10432867

RT 
T1 <cTitle>2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI)</cTitle>: T1 <Title>Machine Learning for Modeling Vegetation Restoration of Forests Using Satellite Images</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-10432867&Exemplar=1&LAN=DE
				
A1 Karimi, Saeideh
A1 Heidari, Mehdi
A1 Mosavi, Amir
YR 2024
SN 2767-9438
K1 Temperature
K1 Computational modeling
K1 Vegetation mapping
K1 Forestry
K1 Mathematical models
K1 Data models
K1 Image restoration
K1 Landsat
K1 vegetation restoration
K1 EVI2 index
K1 datamining
K1 ensemble learning
K1 machine learning
K1 natural hazard
K1 wildfire
K1 fire
K1 ensemble model
K1 deep learning
K1 artificial intelligence
K1 AI
K1 big data
K1 data science
K1 soft computing
K1 applied mathematics
K1 hydrological model
K1 ensemble machine learning
K1 XAI
K1 explainable machine learning
K1 susceptibility mapping
SP 000531
OP 000538
LK http://dx.doi.org/https://doi.org/10.1109/SAMI60510.2024.10432867
DO https://doi.org/10.1109/SAMI60510.2024.10432867
SF ELIB - SuUB Bremen

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