The majority of existing research on urban land cover and land use (LULC) classification from remotely sensed imagery is to differentiate at a specific level (either land cover (LC) or land use (LU)) and at a specific scale. The spatial and hierarchical relationships between LC and LU were not considered in the previous paradigm. This paper proposed Joint Deep Learning as a novel framework for urban LULC classification. Three state-of-the-art methods were compared under the framework, including JDL, SS-JDL and a newly proposed Cross GAN. Experimental results demonstrate the superiority of Cross GAN in terms of classification accuracy and robustness.