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Papers: 10.1007/s10705-017-9870-x

https://doi.org/10.1007/s10705-017-9870-x

Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

Cited by: 170
Author(s): Tomislav Hengl, J.G.B. Leenaars, Keith D. Shepherd, Markus Walsh, G.B.M. Heuvelink, Tekalign Mamo, H. Tilahun, Ezra Berkhout, Matthew Cooper, Eric Fegraus, Ichsani Wheeler, Nketia A. Kwabena
Published: almost 8 years ago

Software Mentions 10

cran: h2o
R Interface for the 'H2O' Scalable Machine Learning Platform
Papers that mentioned: 43
Very Likely Science (100)
cran: mclust
Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation
Papers that mentioned: 297
Very Likely Science (85)
cran: OpenStreetMap
Access to Open Street Map Raster Images
Papers that mentioned: 1,001
Very Likely Science (100)
cran: ranger
A Fast Implementation of Random Forests
Papers that mentioned: 91
Very Likely Science (100)
cran: xgboost
Extreme Gradient Boosting
Papers that mentioned: 207
Very Likely Science (100)
pypi: GDAL
GDAL: Geospatial Data Abstraction Library
Papers that mentioned: 71
Very Likely Science (90)
pypi: h2o
H2O, Fast Scalable Machine Learning, for python
Papers that mentioned: 43
Very Likely Science (100)
pypi: ranger
A Python package for the manipulation of Range objects
Papers that mentioned: 91
Very Likely Science (100)
pypi: VitalSigns
Python Scripts for BNIA-JFIs Vital Signs Data
Papers that mentioned: 3
Very Likely Science (65)
pypi: xgboost
XGBoost Python Package
Papers that mentioned: 207
Very Likely Science (90)