Salīdzināt metodes

	Digitālā augsnes karšu veidošana	Random Forest
Nozare	Agronomija	Mašīnmācīšanās
Saime	Process / pipeline	Machine learning
Izcelsmes gads	Late 1990s – early 2000s (formalised ~2003)	2001
Autors	Multiple contributors; foundational framework by Alex McBratney and colleagues	Breiman, L.
Tips	Spatial prediction and mapping pipeline	Ensemble (bagging of decision trees)
Pirmavots	McBratney, A. B., Mendonca Santos, M. L., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1–2), 3–52.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Citi nosaukumi	DSM, predictive soil mapping, quantitative soil-landscape modelling, geostatistical soil mapping	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Saistītās	1	4
Kopsavilkums	Digital Soil Mapping (DSM) is a quantitative, data-driven pipeline that predicts the spatial distribution of soil properties and classes across a landscape by statistically linking field observations to environmental covariates — terrain attributes, remote sensing imagery, climate surfaces, and geology layers. The approach replaces or augments traditional expert-drawn soil surveys with reproducible, spatially explicit models, and is applied in agronomy, land management, food security, and environmental assessment.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.