Bayesian Maximum Entropy prediction of soil categories using a traditional soil map as soft information
By Dick Brus, Patrick Bogaert, Gerard B. M. Heuvelink. 2008
56 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Civil and Structural Engineering, Environmental Engineering
View Paper: https://www.semanticscholar.org/paper/171ad1b43e7278f032f4f114cb3ab27a8a90a79f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Bas Kempen
- Dick Brus
- Gerard B. M. Heuvelink
- E. Dietz
- Thomas Koschitzki
- Sebastian Osenstetter
- B. Schröder
- Tim Häring
In Civil and Structural Engineering
- Bas Kempen
- Dick Brus
- Gerard B. M. Heuvelink
- E. Dietz
- Thomas Koschitzki
- Sebastian Osenstetter
- B. Schröder
- Tim Häring
In Environmental Engineering
- Bas Kempen
- Gerard B. M. Heuvelink
- Dick Brus
- B. Schröder
- E. Dietz
- Thomas Koschitzki
- Sebastian Osenstetter
- Tim Häring
Citations in the corpus
- Sampling for validation of digital soil maps (2011, methodology): “If the soil map is produced by a statistical method, then a direct estimate of the quality of the soil map is also obtained, well-known examples are the kriging variance and theoretical purity (Webster & Oliver, 2007; Brus et al., 2008).”
- Spatial disaggregation of complex soil map units: A decision-tree based approach in Bavarian forest soils (2012, background): “data as suggested by Brus et al. (2011). Subsequent tomodeling, soil experts described augered soil profiles on defined locations.”