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Rapid extraction of liquefaction induced by strong earthquakes is helpful for earthquake intensity assessment and earthquake emergency response. Supervised classification methods are potentially more accurate and do not need pre-earthquake images. However, the current supervised classification methods depend on the precisely delineated polygons of liquefaction by manual and landcover maps. To overcome these shortcomings, this study proposed two binary classification methods (i.e., random forest and gradient boosting decision tree) based on typical samples. The proposed methods trained the two machine learning methods with different numbers of typical samples, then used the trained binary classification methods to extract the spatial distribution of liquefaction. Finally, a morphological transformation method was used for the postprocessing of the extracted liquefaction. The recognition accuracies of liquefaction were estimated by four evaluation indices, which all showed a score of about 90%. The spatial distribution of liquefaction pits is also consistent with the formation principle of liquefaction. This study demonstrates that the proposed binary classification methods based on machine learning could efficiently and quickly provide the spatial distribution of liquefaction based on post-earthquake emergency satellite images. [ABSTRACT FROM AUTHOR]