IQmulus Workshop on Processing Large Geospatial Data
New emerging data acquisition techniques provide fast and efficient means for multidimensional spatial data collection. All these systems provide point clouds, often enriched with other sensor data, yielding high volumes of raw data.
The IQmulus project organizes a workshop to stimulate researchers from different fields such as Computer Vision, Computer Graphics, Geomatics, Remote Sensing, working on the common goal of processing 3D data, to present state-of-the-art work in the field.
The workshop will take place on July 8th, 2014 in Cardiff (UK), in conjunction with SGP’14. Deadline for contributions: May 23, 2014. The call for contributions can be found here.
The programme will feature outstanding keynote speeches and the presentation of the results of the IQmulus processing contest (details here).
SpatialHadoop: A MapReduce Framework for Spatial Data
Mohamed F. Mokbel, University of Minnesota, US
The talk is about SpatialHadoop; a full-fledged MapReduce framework with native support for spatial data. SpatialHadoop is a comprehensive extension to Hadoop that injects spatial data awareness in each Hadoop layer, namely, the language, storage, MapReduce, and operations layers. In the language layer, SpatialHadoop adds a simple and expressive high level language for spatial data types and operations. In the storage layer, SpatialHadoop adapts traditional spatial index structures, Grid, R-tree and R+-tree, to form a two-level spatial index. SpatialHadoop enriches the MapReduce layer by new components for efficient and scalable spatial data processing. In the operations layer, SpatialHadoop is already equipped with three basic operations, range query, kNN, and spatial join as case studies. Other spatial operations can also be added following a similar approach. We will also discuss various projects that we are carrying, based on SpatialHadoop, to manage NASA satellite data, Twitter data, and OpenStreetMap data.
Point Cloud Data Management
Peter van Oosterom, Delft University of Technology, NL
Point cloud data are important sources for 3D geo-information. Modern acquisition technologies, such as laser scanning, dense image matching from photos or multibeam echo-sounding, generate point clouds with billions or even trillions points especially with repeated scans of same area (the temporal dimension). These point clouds are too massive to be handled efficiently by common geo-ICT infrastructures. Therefore, core support for point cloud data types in the existing spatial DBMS is needed, besides the existing vector and raster data types. Further, a new and specific web-services protocol for point cloud data is investigated, supporting progressive transfer based on multi-resolution. The eScience project investigates solutions in order to better exploit the rich potential of point cloud data. The project partners are: Rijkswaterstaat, Fugro, Oracle, Netherlands eScience Centre and TU Delft. An inventory of the user requirements has been made using structured interviews with users from different background: government, industry and academia. Based on these requirements a benchmark has been developed to compare various point cloud data management solutions w.r.t. functionality and performance. The main test data set is the second national height map of the Netherlands, AHN2, with 6 to 10 samples for every square meter of the country, resulting in more than 100 billion points with 3 cm accuracy. The AHN2 data is specified and financed by the Dutch government (Rijkswaterstaat and regional Water boards) and produced on contract basis by engineering firms, such as Fugro. Initially for water management applications (flood modelling, dike monitoring), but more and more other government, commercial and scientific applications are developed (forest mapping, generation of 3D city models, etc.).