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Unmanned Aerial Vehicle (UAV) technique has been widely utilized in geohazards assessment. In this work, the effectiveness of the UAV photogrammetry in the remote sensing and assessment of the landslide behavior is demonstrated through a case study of a landslide that occurred in Guizhou, China on 10 June 2018. The post-landslide assessments were conducted through a field investigation by a team of experts; and, two UAV photogrammetry-based surveys were carried out. On the basis of a detailed inspection of the high-resolution aerial photographs collected from the UAV photogrammetry and historical satellite imagery, subsurface stratigraphic configuration revealed from borehole explorations and the local rainfall data collected, the failure mechanism of this landslide is investigated. The occurrence of this landslide is probably attributed to the combined influence of the long-term rainfall and the engineering activities at this site. An automatic landslide cracks recognition model, which is based on the deep learning-based image recognition technique of RetinaNet, is further developed to map the cracks at the landslide site. The effectiveness of this automatic crack recognition model is validated through quantitative comparisons between the landslide cracks recognized and the field survey results. Based upon the landslide cracks identified on two different dates after this landslide event (by the field survey and automatic crack recognition model) and the subsurface displacement revealed from a drilled hole, the evolution behavior of this landslide is analyzed. The results show that the stability of this landslide was not achieved during the first slide in June 2018 and the limit of the landslide was increased much from June to November 2018. In such a situation, the elements at risk in the zones that are potentially impacted by this landslide are identified (with the aid of the UAV images collected), and a preliminary consequence assessment is conducted. • The failure mechanism of a landslide in Guizhou, China is investigated. • An automatic landslide cracks recognition model is developed using RetinaNet. • The landslide behavior is studied based on the cracks recognized on two dates. • A UAV images-based preliminary assessment of the landslide consequence is made. [ABSTRACT FROM AUTHOR]