Purpose The purpose of this paper is to convert real-world raster data into vector format and evaluate loss of accuracy in the conversion process. Open-source Geographic Information System (GIS) is used in this process and system resource utilizations were measured for conversion and accuracy analysis methods. Shape complexity attributes were analyzed in co-relation to the observed conversion errors. Design/methodology/approach The paper empirically evaluated the challenges and overheads involved in the format conversion algorithms available in open-source GIS with real-world land use and land cover (LULC) map data of India. Across the different LULC categories, geometric errors of varying density were observed in Quantum GIS (QGIS) algorithm. Area extents of original raster data were compared to the vector forms and the shape attributes such as average number of vertices and shape irregularity were evaluated to explore the possible correlation. Findings The results indicate that Geographic Resources Analysis Support System provides near error-free conversion algorithm. At the same time, the overall time taken for the conversion and the system resource utilizations were optimum as compared to the QGIS algorithm. Higher vector file sizes were generalized and accuracy loss was tested. Research limitations/implications Complete shape complexity analysis could not be achieved, as the weight factor for the irregularity of the shapes is to be varied based on the demography as well as on the LULC category. Practical implications Because of the higher system resource requirements of topological checker tool, positional accuracy checks for the converted objects could not be completed. Originality/value This paper addresses the need of accuracy analysis of real-world spatial data conversions from raster to vector format along with experimental setups challenges and impact of shape complexity.