Object-oriented mapping of landslides using Random Forests

By André Stumpf, Norman Kerle. 2011

692 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Management, Monitoring, Policy and Law, Media Technology, Remote Sensing

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Citations in the corpus

Measuring the Effectiveness of Various Features for Thematic Information Extraction From Very High Resolution Remote Sensing Imagery (2015, background): “OVER the last decade, very high resolution (VHR) remote sensing images have been widely applied to thematic information extraction [1], such as landslide mapping [2], road extraction [3], [4], and precipitation mapping [5].”
Testing the reliability and stability of the internal accuracy assessment of random forest for classifying tree defoliation levels using different validation methods (2015, result): “The study similar to other studies has shown that the use of split lower than 60–40% or greater than 70–30% for defoliation classification is not recommended (Stumpf and Kerle 2011; Rodriguez-Galiano et al. 2012).”
GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran (2015, positive): “…and regression tree (CART), and random forest (RF) techniques have been successfully applied in landslide susceptibility and hazard mapping (Stumpf and Kerle 2011a, b; Vorpahl et al. 2012; Trigila et al. 2013), gully susceptibility mapping (Bou Kheir et al. 2007; Geissen et al. 2007;…”
Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN) (2018, methodology): “…used data mining algorithms including boosted regression tree, classi ﬁ cation and regression tree, and random forest models (Miner et al. 2010; Stumpf and Kerle 2011; Vorpahl et al. 2012; Catani et al. 2013; Micheletti et al. 2014; Youssef 2015) have been implemented for landslide…”
Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification (2013, background): “…data and feature selection) and tends to yield high accuracy (Breiman 2001); second, it works well with high variance among individual trees (Stumpf and Kerle 2011); and third, it has good efficiency in dealing with large numbers of features without feature deletion (Rodriguez-Galiano et…”
Implementation of machine-learning classification in remote sensing: an applied review (2018, background): “Stumpf and Kerle (2011), who investigated the mapping of landslides with an imbalanced training sample that contained more examples of non-landslide locations than landslide locations, found that RF underestimated landslide occurrence.”