Traditional methods for updating geographic information data involve long cycles and a large amount of manual work, making it challenging to maintain the data’s timeliness and limiting its utility and scope. Timely updates require rapid change detection. With the development of aerial and satellite sensors, as well as Internet and social sensing technologies, rapid change detection becomes possible, shifting the approach from a “push-scan” method to a “change-discovery-driven” model. In this study, we introduce a change-discovery-driven geographic information update model using crowdsourced data, supported by a change-detection platform. The platform consolidates change detection, standardized processing, and shared services to facilitate timely updates. For the first time, multiple channels for change detection are comprehensively established, including automatic change detection from remote sensing images, web crawlers, crowdsourcing, and business information aggregation. These methods efficiently extract change information and provide services in the form of change spots, offering robust support for rapid geographic information updates. This approach has been applied in basic mapping updates and land change surveys, enabling quick identification of changes in geographic information. The change-discovery-driven model significantly enhances the efficiency of geographic information updates, reducing manpower and minimizing repetitive operations.