Against the backdrop of rapid urbanization in China, enhancing public participation at the community level has become an important national strategy. It is necessary for planners to deploy new approaches to understand public needs and to identify common issues or areas within the community in a timely manner. Participatory sensing, which builds upon the widespread information sharing using the Internet and mobile technology, has provided new opportunities for planners to sense and analyze public sentiment, human mobility patterns and surrounding environments at better spatiotemporal resolutions. This opportunity has gained considerable attention from research community and has spurred a range of studies on topics such as emerging public participation paradigm and urban computing. However, current studies have not systematically investigated the mechanisms and common approaches of implementing participatory sensing in the context of urban community planning. This paper bridges this research gap by proposing a conceptual framework for studying participatory sensing in the community planning context, and by developing a technological framework for processing, integrating and analyzing multi-sourced human sensory data. Particularly, the conceptual framework builds upon the theories of public participation, the principles of participatory sensing, and the inter-relationships among residents, urban communities and urban community planning. A technical framework that synthesizes sensing, computing and application is further proposed. Specifically, sensing refers to collecting various data about how residents use and perceive urban community space; computing refers to extracting useful knowledge regarding human activities and perceptions, individual’s biological information and environments from raw sensed data; application refers to analyzing extracted knowledge for supporting community planning compilation and decision-making. Based on the framework, the workflows of extracting and spatializing residents’ subjective perception, analyzing how different population groups use urban community space, and apply participatory sensing and computing for urban community planning compilation and decision-making are further suggested. The proposed workflows build upon multi-disciplinary methods and aim to shed light on further developments of relevant methods and techniques for utilizing multi-sourced data that support urban community planning. Overall, this study will contribute to the methodological developments of applying participatory sensing for urban community planning. It will also shed light on future developments of new practical approaches for enhancing public participation, and for supporting rational planning evaluation and decision-making in urban communities.