Crowdsourcing, as one of the most promising tech-niques for distributed problem-solving, requires sustained human involvement. Therefore, it also brings new challenges to data management, fundamentally data input and its quality. In this paper, we looked at various forms of user motivations and quality control of crowdsourcing when building accessibility maps mobile applications. We discuss how motivations could be used to contribute to our accessibility maps scenarios, and how data can be improved for two types of participants: individual participants and organization participants. We identified three useful techniques for improving data quality: qualification-based, reputation-based, and aggregation-based. In addition, based on our own mobile application (named WEMAP), we evaluated our approaches through focus group discussions and in-depth interviews.