Sedimentation is one of the major operational hazards for culverts located in Midwestern areas of the United States. Currently, there are no systematic efforts for mitigating detrimental effects of sediment blockage at culverts, as the problem involves complex and interlinked environmental processes that are difficult to investigate and solve with conventional approaches (e.g., experimental methods and physical modeling). In this paper, we present a new, innovative solution for addressing culvert sedimentation using a data-driven approach. This approach enables the identification of culvert sedimentation drivers and quantifies the relationships between drivers and culvert sedimentation severity, making use of an extensive set of multi-disciplinary data. The data analysis and management tools are embedded in a web-based platform, the IowaDOT Culverts. Through user-friendly interfaces and interactive visualizations, this platform facilitates the analysis and forecasting of sedimentation at multi-box culverts across Iowa. The IowaDOT Culverts web-based geospatial platform allows us to (a) integrate, access, store, and manage diverse culvert-related information; (b) organize and document culvert inspections in real time; (c) conduct sedimentation-related analyses using computer-driven visual analytical approaches; and (d) support forecasting of the culvert sedimentation potential. The platform is developed with open-source web technologies and modular system designs, making it scalable, flexible, and extendable to other geographical regions.