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The application of deep learning methods has brought improvements to the accuracy and automation of landslide extractions based on remote sensing images because deep learning techniques have independent feature learning and powerful computing ability. However, in application, the quality of training samples often fails the requirement for training deep networks, causing insufficient feature learning. Furthermore, some background objects (e.g., river, bare land, building) share similar shapes, colors, and textures with landslides. They can be confusing to automatic tasks, contributing false and missed extractions. To solve the above problems, a background-enhancement method was proposed to enrich the complexity of samples. Models can learn the differences between landslides and background objects more efficiently through background-enhanced samples, then reduce false extractions on background objects. Considering that the environments of disaster areas play dominant roles in the formation of landslides, landslide-inducing attributes (DEM, slope, distance from river) were used as supplements, providing additional information for landslide extraction models to further improve the accuracy of extraction results. The proposed methods were applied to extract landslides that occurred in Ludian county, Yunnan Province, in August 2014. Comparative experiments were conducted using a mask R-CNN model. The experiment using both background-enhanced samples and landslide-inducing information showed a satisfying result with an F1 score of 89.08%. Compared with the F1 score from the experiment using only satellite images as input data, it was significantly improved by 22.38%, underscoring the applicability and effectiveness of our background-enhancement method. [ABSTRACT FROM AUTHOR]