Geospatial data is getting bigger and such large and complex datasets are becoming more and more difficult to process by using traditional systems and methods, such as individual workstations and single-threaded applications. Numerous spatial computing solutions have been developed to tackle this challenge by enabling distributed data stores, parallel and distributed computing capabilities, and special computing units (e.g., GPU/TPU) to enable discovery, delivery, analysis, and visualisation of geospatial data. However, these solutions require specialized know-how and expertise, as well as access to adequate computing infrastructure that is mostly located remotely in the Cloud. Therefore, a transition in modus operandi is necessary. The Geospatial Computing Platform lowers the barrier by providing a state-of-the-art computing infrastructure designed for (big) geospatial analysis tasks that combines low-energy, high-performance Edge AI units with powerful GPU-enabled big data computing units in a seamless and innovative manner. Through the platform the users can access thousands of scientific software packages (e.g. Python / R) that are kept up to date regularly. Public datasets available platform-wide improve data access and reduce data duplication, whereas shared workspaces allow research groups to work in a collaborative manner. Beside a modern interactive notebook interface, the platform also allows remote desktop access for desktop applications (e.g., QGIS, SNAP) and features integrated geospatial database, map serving, and data collection services to benefit from existing well-established tools and technologies. This talk will provide information about the design and architecture of the platform, current use cases, and lessons learned during the operation period of two years, involving 200,000+ hours of multi-core/GPU computation and a user community of more than 800 users.