Land Use
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2022 ◽  
Vol 114 ◽  
pp. 105967
Author(s):  
Rafael Delgado-Artés ◽  
Virginia Garófano-Gómez ◽  
José-Vicente Oliver-Villanueva ◽  
Eduardo Rojas-Briales

2022 ◽  
Vol 507 ◽  
pp. 120007
Author(s):  
I. Drobyshev ◽  
N. Ryzhkova ◽  
M. Niklasson ◽  
A. Zhukov ◽  
I. Mullonen ◽  
...  

2022 ◽  
Vol 327 ◽  
pp. 107830
Author(s):  
Esra H. Sohlström ◽  
Ulrich Brose ◽  
Roel van Klink ◽  
Björn C. Rall ◽  
Benjamin Rosenbaum ◽  
...  

2022 ◽  
Vol 114 ◽  
pp. 105937
Author(s):  
Aaron Deslatte ◽  
Katarzyna Szmigiel-Rawska ◽  
António F. Tavares ◽  
Justyna Ślawska ◽  
Izabela Karsznia ◽  
...  

Author(s):  
Subhra Swetanisha ◽  
Amiya Ranjan Panda ◽  
Dayal Kumar Behera

<p>An ensemble model has been proposed in this work by combining the extreme gradient boosting classification (XGBoost) model with support vector machine (SVM) for land use and land cover classification (LULCC). We have used the multispectral Landsat-8 operational land imager sensor (OLI) data with six spectral bands in the electromagnetic spectrum (EM). The area of study is the administrative boundary of the twin cities of Odisha. Data collected in 2020 is classified into seven land use classes/labels: river, canal, pond, forest, urban, agricultural land, and sand. Comparative assessments of the results of ten machine learning models are accomplished by computing the overall accuracy, kappa coefficient, producer accuracy and user accuracy. An ensemble classifier model makes the classification more precise than the other state-of-the-art machine learning classifiers.</p>


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