H2020 EINFRA nD-PointCloud

Deeply Integrated and Semantic Extended nD-PointCloud

We propose the nD-PointCloud model as a breakthrough in handling massive multidimensional point cloud data sets in the whole range from data ingestion, data management, to data analytics and visualisation. The data represent space, time and added information such as colour, material properties, velocity, etc. State of the art spatio-temporal representations are based on either gridded (raster, voxel) or object (vector) models. In many cases these representations are organized in a fixed number of levels of importance (detail/scale), which introduces serious limitations: fixed level choices and data density jumps between levels. The proposed nD-PointCloud model facilitates continuous instead of fixed levels of importance. The continuous level of importance value of a point can be regarded as an added dimension to space and time. Original and novel aspects of our nD-PointCloud model:

1. defining organizing and property dimensions,
2. offering attribute granularity: from individual point to group level,
3. obtaining the continuous level of importance value for a point,
4. aggregating points to higher level points,
5. representing and assessing effect of different coordinate reference systems: spherical or Cartesian. 

We will explore: nD space filling curves, tree structures, etc. to realize the deep integration of space, time and importance as basis for data organization and apply high performance/throughput computing for big data (trillions of points). By enabling operations directly on the raw point cloud data, nD-PointCloud largely avoids and/or alleviates the extract, transform, load hurdle, which is an increasingly serious problem in the era of big data. The deep integration enables semantically richer point cloud data functionality, which broadens the opportunities for knowledge discovery. We expect major advances in domains requiring lossless spatio-temporal data of extremely high accuracy, such as geo information and astronomy which are used as Proof-of-Principle.

Project Partners:

1 Delft University of Technology TUD NL Peter van Oosterom
2 Netherlands eScience Center NLeSC NL Oscar Martinez Rubi
3 MonetDB Solutions B.V. MDBS NL Ying Zhang
4 Vienna University of Technology TUW AUT Michael Wimmer
5 Jülich Supercomputing Centre / University of Iceland JUELICH DE/IS Morris Riedel
6 Institut d'Estudis Espacials de Catalunya IEEC ES Xavier Luri
7 Institut National de l'Information Géographique et Forestière IGN-F FR Didier Richard

User Board Members:

1 Birkbeck, University of London BBK GB Elliot Sefton-Nash
2 Fugro Fugro NL Martin Kodde
3 Institute of Theoretical Astrophysics at the University of Oslo UIO NOR Bridget Falck
4 Hasso Plattner Institute HPI DE Rico Richter
5 Oracle Corporation Oracle USA Hans Viehmann
6 Universiteit van Amsterdam UVA NL Wouter Los
7 Cyclomedia Cyclomedia NL Bart Beers
8 Australian National University ANU AU Ben Evans
9 NASA/University of Maryland UoM USA Kwo-Sen Kuo
10 Open Geospatial Consortium OGC UK Bart De Lathouwer
11 Google Google UK Ed Parsons
12 Univesity College London UCL GB Jan Boehm
13 Institut Cartogràfic i Geològic de Catalunya ICGC ES Antonio Magariños
14 Wuhan University WHU CHI Jianya Gong

The user board members have provided letter to support the nD-PointCloud proposal. Click here to read them