Spatial Data Management

Manage and process Digital Terrain Models

Lidar, 3D Laser scanners and photography are used to map and monitor land areas and objects. These measurements result in point clouds and raster datasets. A point cloud consists of large amounts, often millions, of 3D points with per point one or more attributes. 

Photography and satellites deliver raster datasets. To reduce the amount of data point clouds are sometimes resampled to raster datasets. The combination of multiple measurement datasets for an application is a Digital Terrain Model (DTM).

Point clouds and raster datasets  

Point clouds and raster datasets are easy to import and manage in GeolinQ. GeolinQ supports LAS, ASCII as well as GeoTiff as import file formats. Multiple attributes per point are supported. These attributes are to be configured by the user and must be linked to the attributes of the point in the input file. GeolinQ supports all EPSG coordinate transformation so virtually every point cloud in any coordinate systems can be imported.      

Combining datasets to Digital Terrain Models 

For DTM’s it is needed to combine multiple dataset to a single dataset holding the best available measured data in a specific area. The Seamless Point Surface (SPS) concept in GeolinQ offers functionality for automatic and flexible compilation of seamless data models based on multiple point cloud and raster datasets. Based on the metadata attributes of the datasets users can configure priority rules. Based on these rules the overlap between the datasets are removed and the hull of the datasets in the SPS determined resulting to the best available data on any location. New imported surveys are automatically integrated by SPS based on the configured selection and priority rules guaranteeing an always up-to-date DTM.

Using DTM's

Functionality is offered to configure color scales to visualize the data, calculate contours and export DTM’s. In addition DTM’s can be made available as map layers and data export options to end-users via Applicatiesservices.     

High Performance

Point cloud datasets are generally large and may contain millions or even a billion of points. Efficient storage, indexing, tiling and dynamic data pyramiding are used to allow fast retrieval and visualisation at all scale levels.