Wireless signal strength fingerprinting has become an in- creasingly popular technique for realizing indoor localization systems using existing WiFi infrastructures. However, these systems typically re- quire a time-consuming and costly training phase to build the radio map. Moreover, since radio signals change and fluctuate over time, map main- tenance requires continuous re-calibration. We introduce a new concept called “asynchronous interval labeling” that addresses these problems in the context of user-generated place labels. By using an accelerometer to detect whether a device is moving or stationary, the system can con- tinuously and unobtrusively learn from all radio measurements during a stationary period, thus greatly increasing the number of available sam- ples. Movement information also allows the system to improve the user experience by deferring labeling to a later, more suitable moment. Initial experiments with our system show considerable increases in data col- lected and improvements to inferred location likelihood, with negligible overhead reported by users.