Assessment of accuracy for urban classified raster map analysis

The rapid growth of urban space and its environmental challenges require precise mapping techniques to represent complex earth surface features more accurately. In this study, we examined four mapping approaches (unsupervised, supervised, fuzzy supervised and GIS post-processing) using SPOT image to predict urban land use and land cover of Mile city, Yunnan province, China. A new stratified sampling method was chosen to generate geographic reference data for each map to assess the accuracy. The accuracies of the maps were measured. The GIS post-processing approach proposed in this research improved the mapping results, showing the highest overall accuracy of 88.50% as compared to other approaches. The fuzzy supervised approach yielded a better accuracy (85.75%) than the supervised and unsupervised approaches. This paper presents the strengths of the mapping approaches and the potentials of the sensor for mapping urban areas, which may help urban planners monitor and interpret complex urban characteristics.