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	Mapowanie cyfrowe gleb	Random Forest
Dziedzina	Agronomia	Uczenie maszynowe
Rodzina	Process / pipeline	Machine learning
Rok powstania	Late 1990s – early 2000s (formalised ~2003)	2001
Twórca	Multiple contributors; foundational framework by Alex McBratney and colleagues	Breiman, L.
Typ	Spatial prediction and mapping pipeline	Ensemble (bagging of decision trees)
Źródło pierwotne	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.
Inne nazwy	DSM, predictive soil mapping, quantitative soil-landscape modelling, geostatistical soil mapping	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Pokrewne	1	4
Podsumowanie	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.