Accurate land cover mapping provides important scientific support for ecological environment protection and sustainable urban development. However, given the high expense of acquiring training samples and the difficulty of fully utilizing remote sensing big data, large-scale time-series land cover mapping remains to be a challenge. To address the issue, we proposed a novel time-series large-scale mapping approach that obtains high-quality training samples from OpenStreetMap volunteered data and transfers them for land cover mapping in histori-cal years. Relying on the data archived on the Google Earth Engine platform, we constructed a discriminating feature set that contains spectrum, texture, and backscatter coefficient, among others. Taking the Guangdong -Hong Kong-Macao Greater Bay Area as a case area, the annual land cover maps after spatio-temporal consis-tency modification from 1986 to 2021 were obtained. The validation samples proved that the derived land cover classification results have high accuracy. We verified the reliability and superiority of the proposed approach by comparing our results with existing land cover products. The proposed mapping approach owns great trans-ferability, and the mapping results provide a valid decision-making basis for urban planning.