With the expansion of complex geographic calculations and the increase of spatial data types involved in the spatial analysis of large areas, the need becomes urgent for fast rasterization of massive multi-source geographic vector data. A parallel scanline algorithm is proposed for rapid rasterization. It provides a systematic solution to solve the complicated situation in parallel processing (cross-processor boundaries, common boundaries, and tiny polygons), thus ensuring the accuracy of the parallel scanline algorithm. The relationship of parallel speedup with the number of processors, the data partition pattern, and the raster grid size is discussed. Massive vector geographic data (approximately 0.7 million polygons) used in the experiment were effectively processed, thereby dramatically reducing the processing time and getting good speedup.