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This paper presents a semi-supervised Stochastic Expectation-Maximization (SEM) algorithm for detailed urban land cover mapping using multitemporal high-resolution polarimetric SAR (PolSAR) data. By applying an adaptive Markov Random Field (MRF) with the spatially variant Finite Mixture Model (SVFMM), spatial-temporal contextual information could be effectively explored to improve the mapping accuracy with homogenous results and preserved shape details. Further, a learning control scheme was proposed to ensure a robust semi-supervised mapping process thus avoiding the undesired class merges. Four-date RADARSAT-2 polarimetric SAR data over the Greater Toronto Area were used to evaluate the proposed method. Common PolSAR distribution models such as Wishart, G0p, Kp and KummerU were compared through this contextual SEM algorithm for detailed urban land cover mapping. Comparisons with Support Vector Machine (SVM) were also conducted to assess the potential of our parametric approach. The results show that the Kp, G0p and KummerU models could generate better urban land cover mapping results than the Wishart model and SVM.