A user’s location information is commonly used in diverse
mobile services, yet providing the actual name or semantic
meaning of a place is challenging. Previous works required
manual user interventions for place naming, such as search-
ing by additional keywords and/or selecting place in a list.
We believe that applying mobile sensing techniques to this
problem can greatly reduce user intervention. In this paper,
we present an autonomous place naming system using op-
portunistic crowdsensing and knowledge from crowdsourc-
ing. Our goal is to provide a place name from a person’s
perspective: that is, functional name (e.g., food place, shop-
ping place), business name (e.g., Starbucks, Apple Store),
or personal name (e.g., my home, my workplace). The main
idea is to bridge the gap between crowdsensing data from
smartphone users and location information in social network
services. The proposed system automatically extracts a wide
range of semantic features about the places from both crowd-
sensing data and social networks to model a place name. We
then infer the place name by linking the crowdsensing data
with knowledge in social networks. Extensive evaluations
with real deployments show that the proposed system out-
performs the related approaches and greatly reduces user
intervention for place naming.