Radio map, serving as an efficient indicator of wireless environments, has been widely used in smart-city applications, including network monitoring/planning, anomaly signal detection, and indoor/outdoor localization. It is hard to maintain an update-to-date fine-grained radio map within a large area, since the radio map changes rapidly due to the internal and external factors. Previous studies usually relied on time-consuming site surveys at densely predefined reference points, leading to either coarse-grained or out-of-date radio maps. In this paper, we propose a fine-grained radio map reconstruction framework, called Supreme, based on crowd-sourced data in an image super-resolution manner. Specifically, Supreme explores spatial-temporal relationships in historical coarse-grained radio maps and builds a real-time fine-grained radio map using deep spatial-temporal reconstruction networks. Furthermore, a heterogeneous data fusion module is devised to make full use of external information. To evaluate the performance of Supreme, we conduct extensive experiments and ablation studies on a large-scale dataset with a total of six-month data collected from two university campuses. Besides, we investigate the transferability of Supreme in different locations and service networks, showing that the fine-tuned model can largely reduce the training time and achieve better performance. Experimental results demonstrate that our model outperforms state-of-the-art baselines and a case study on the localization is enhanced with marginal improvements on accuracy.