Land cover mapping is an important aspect in remote sensing (RS) researches, and the precision validation is a necessary procedure to describe the quality of land cover data interpreted from RS images. GPS pointing combined with the field photos are mainly used in traditional validation methods, which is easily and subjectively affected by the field work efficiency and sampling place selection. A new land cover data precision validation method is proposed in this study, which is based on the vehicle-mounted three dimensional (3D) camera. A series of detailed technical issues are addressed, which include the outputs and extraction of the validated GPS points from 3D videos, the identification and management of these points, the precision evaluation technology based on the grading system, and so on. 264 GB videos were used, which were gathered in the field validation task from 7 provinces of eastern Mongolia in 2013. Taking 1, 5 and 10 seconds, and 1 and 5 minutes respectively as the sampling intervals, collecting at an average car speed of 50. 95 kilometers per hour, 123396, 24679, 12339, 2056 and 411 points were recognized accordingly. The precision validation results of the study area were obtained based on the scoring methods. The top score of overall accuracy is 71.01% with the 1 minute interval, the accuracies for meadow steppe, real steppe, desert steppe, built area, barren, cropland and water are 49.29%, 86.09%, 32.71%, 80.65%, 87.5%, 1 and 0, respectively. The analysis shows that the 3D videos are suitable for land cover validation due to its highly automatic and continuous working features in the information and Big Data era. The testing results of the field sampling with various temporal intervals indicate that 1 minute interval is optimal for the precision validation in eastern Mongolia. For improving the future studies, sampling with variable temporal interval is the key issue, and it should be advanced thoroughly for the video data automatic processing technology.