In this paper, we proposed a novel task-consistency learning method that allows training a vacant space detection network (target task) based on the logistic consistency with the semantic outcomes from a naive flow-based motion behavior classifier (source task) in a parking lot. By well designing the reward mechanism upon semantic consistency, we show the possibility to train the target network in a reinforcement learning setting. Compared with conventional supervised detection methods, the major contribution of this work is to learn a vacant space detector via semantic consistency rather than supervised labels. The dynamic learning property may make the proposed detector been deployed in different lots easily without heavy training loads. The experiments show that based on the task consistency rewards from the motion behavior classifier, the vacant space detector can be trained successfully.