Latest release of Global Mapper SDK

28 November 2018

Blue Marble Geographics announced the immediate availability of version 20 of the Global Mapper Software Development Kit (SDK) along with the accompanying LiDAR Module SDK.
Mirroring the most important capabilities of the desktop version of the software, this  toolkit provides software engineers with the means to embed the latest geospatial technology into their custom applications.

Among the highlights of the version 20 release are dramatically improved vector data performance in both the 2D and 3D environments, updated 3D mesh rendering with colors now displayed in the 2D view, and faster display and export of online tiled datasets. Additional upgrades to the SDK functionality include improved box resampling of color images, especially those with palettes; several new supported formats, including Cyclone PTX and Autodesk Recap (RCP and RCS) point clouds; new projections and datums, including GDA2020 (Australia) and TUREF (Turkey); and support for Intermap’s online NextMap worldwide elevation dataset.

For users of the Global Mapper LiDAR Module, the version 20 SDK release also introduces a wealth of new and updated functionality. Point clouds can now be thinned, from both a 2D and 3D perspective, reducing file size and improving efficiency; a gridded layer can now be created from the classification values associated with LiDAR points; and a new scripting option has been added to apply colors to a point cloud from underlying imagery. 

“The Global Mapper SDK has become one of the most important components of Blue Marble’s suite of geospatial products,” stated Patrick Cunningham, Blue Marble President. “Motivated by the rapid emergence of the desktop software as a major player in the GIS industry, developers are increasingly turning to the corresponding SDK to leverage the software’s powerful geoprocessing tools in a wide variety of third party applications. The improved data handling capability of the version 20 release demonstrates our commitment to providing tools that work efficiently with even the largest datasets.” 

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