This innovative eScience technology project aims for making point clouds the primary representation of spatio-temporal features, throughout the whole processing chain: data acquisition, storage, analysis, visualization and dissemination. Today point clouds are mainly used in the data acquisition phase; gridded (raster) or object (vector) models are used in the other phases. Handling the extract-transform-load actions becomes an increasing problem in using big data. Based on a novel use of high-resolution nD space filling curves this project will realize deep integration of space, time and scale as basis for data organization, and enable High Performance/Throughput Computing for enormous point clouds. By enabling operations directly on the raw point cloud data, nD-PointCloud realises major advances in domains requiring lossless spatio-temporal data of extremely high accuracy. A distributed Open Point Cloud Map (OPCM) infrastructure will be developed that supports data sharing of big data nD-PointCloud and enables interactive real-time visualization using perspective views without data density shocks, continuous zoom-in/out and progressive data streaming between server and client. Applications from the domain of water management are used as Proof-of-Principle. If successful, nD-PointCloud will become the preferred model enabling progress in (research) fields like cultural heritage, land administration, vegetation monitoring, building modelling,transportation and mobility.