Tomislav Hengl
Environmental Engineering · 2003–2018
A researcher in the Science corpus: 16 papers; trusts 51 researchers and is trusted by 35 researchers here, as inferred from the citation graph.
Subfields
- Environmental Engineering — 87 citations into it; #272 of 5,479 by trust here, #138 by citations
- Ecology — 48 citations into it; #1,136 of 10,717 by trust here, #2,377 by citations
- Artificial Intelligence — 38 citations into it; #30,605 of 35,345 by trust here, #25,809 by citations
- Global and Planetary Change — 27 citations into it
- Soil Science — 26 citations into it
- Management, Monitoring, Policy and Law — 23 citations into it; #475 of 2,821 by trust here, #172 by citations
- Economics and Econometrics — 22 citations into it
- Statistics and Probability — 22 citations into it
Whom Tomislav Hengl trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex B. McBratney
- Noel Cressie
- Gerard B. M. Heuvelink
- Budiman Minasny
- Claude E. Shannon
- Daniel A. Griffith
- Christopher D. Lloyd
- Dick Brus
- Thomas F. A. Bishop
- Edzer J. Pebesma
Who trusts Tomislav Hengl overall
- Gerard B. M. Heuvelink
- Dick Brus
- Jin Li
- Mahyar Yousefi
- Budiman Minasny
- El-Sayed Ewis Omran
- Michele Dalponte
- Alex B. McBratney
- Madlene Nussbaum
- Hanna Meyer
Papers from Tomislav Hengl's perspective
Ordered by how strongly the researchers Tomislav Hengl trusts endorse each paper. Same corpus, a different order for every researcher.
- Bagging Predictors
- ImageNet classification with deep convolutional neural networks
- Random Forests
- Gradient-based learning applied to document recognition
- Regression Shrinkage and Selection via the Lasso
- The elements of statistical learning: data mining, inference and prediction
- On spatial prediction of soil properties in the presence of a spatial trend: the empirical best linear unbiased predictor (E‐BLUP) with REML
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Generalized Linear Models
Their papers
- Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables (2018) — 919 citations
- Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation (2018) — 446 citations
- A global map of mangrove forest soil carbon at 30 m spatial resolution (2018) — 346 citations
- Soil carbon debt of 12,000 years of human land use (2017) — 1,196 citations
- Mapping the global depth to bedrock for land surface modeling (2017) — 314 citations
- SoilGrids250m: Global gridded soil information based on machine learning (2017) — 3,747 citations
- Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions (2015) — 794 citations
- SoilGrids1km — Global Soil Information Based on Automated Mapping (2014) — 1,112 citations
- A Practical Guide to Geostatistical Mapping (2009) — 441 citations
- Geomorphometry: Concepts, software, applications (2009) — 337 citations
- About regression-kriging: From equations to case studies (2007) — 956 citations
- Methods to interpolate soil categorical variables from profile observations: Lessons from Iran (2007) — 168 citations
- Finding the right pixel size (2006) — 805 citations
- A generic framework for spatial prediction of soil variables based on regression-kriging (2004) — 1,074 citations
- Supervised Landform Classification to Enhance and Replace Photo‐Interpretation in Semi‐Detailed Soil Survey (2003) — 116 citations
- Soil sampling strategies for spatial prediction by correlation with auxiliary maps (2003) — 138 citations