Extracting semantic meaning of locations enables a large range of applications including automatic daily activity logging, assisted living for elderly, as well as the adaptation of phone user profiles according to user needs. Traditional location recognition approaches often rely on power-hungry sensor modalities such as GPS, network localization or audio to identify semantic locations, e.g., at home, or in a shop. To enable a continuous observation with minimal impact on power consumption, we

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... ropose to use low-power ambient sensors -pressure, temperature, humidity and light -integrated in phones. Ambient fingerprints allow the recognition of routinely visited places without requiring traditional localization sensing modalities. We show the feasibility of our approach on 250 hours of data collected in realistic settings by five users during their daily transition patterns, in the course of 49 days. To this end, we employ a prototype smartphone with integrated humidity and temperature sensor. We achieve up to 80% accuracy for recognition of five location categories in a user-specific setting, while saving up to 85% of the battery power consumed by traditional sensing modalities.