Abstract: Earthquakes can trigger numerous and widely distributed landslides. Rapid mapping of landslides with high accuracy is crucial for understanding their spatial patterns, and assessing landslide hazards and risks. Visual interpretation or automatic recognition are conventional methods for earthquake-triggered landslide mapping. Due to the influence of post-earthquake rainfall, cloud coverage rate of high-resolution optical remote sensing images is generally high, which limit investigation and assessment of earthquake-triggered landslides. Based on the Google Earth Engine platform, this work proposed a method for removing cloud and cloudless images using Sentinel-2 time series data and the cloud probability dataset. Through comparing performance of different time period, Sentinel-2 time series from 14th August 2021 to 31th January 2022 were chosen with 20% cloud coverage rate. This work masked all pixels covering by cloud and replaced them with cloud-free pixels in the images of adjacent time, producing high quality cloudless mosaic images covering the study area. The proposed method can improve the quality of post-earthquake images effectively. Then, an object-oriented model for earthquake-triggered landslide recognition is constructed based on the cloud-free mosaic images. Firstly, the multiscale segmentation algorithm was applied to the cloudless mosaic images to generate numerous objects. Then, multiple features were calculated based on every object, including spectral features, vegetation index and spatial proximity relationship between adjacent objects belonging to different class. Finally, earthquake-triggered landslides were classified used membership function based on thresholds of multiple features. Taking 2021 Haiti Nippes Earthquake as a case study, this work implemented the proposed method. Meanwhile, evaluation of classification results was carried out based on using manually interpreted landslides using high-resolution Planet images with 3-meter resolution in the validating area. The precision of the method is 77.5%, recall is 52.77% and F₁ index is 62.79%. The results show that the proposed methods benefit earthquake-triggered landslides detection in the cloudy area and can provide technical support for post-disaster emergency investigation and assessment.