2019 FOSS4G Bucharest Talks speaker: Markus Metz


How digging into the earth for the fibre roll-out took GRASS to the cloud

Deutsche Telekom AG (DTAG) revolutionises the planning process of fibre networks (FTTH) to
* increase number of connected households and industries dramatically, and
* shorten the time to market in general.

For this goal DTAG, advanced geoprocessing algorithms including artificial intelligence (AI) automate the conversion of surface information into cost optimised potential trenches used in the fibre build process. The surface information is gathered by terrestrial laser scanners and photos as well as extracted through a complex analysis from aerial orthophotos. This surface information then is used in conjunction with other (open) data as an input for the provisioning of potential trenches needed for FTTH. The processing of these orthophotos as well as the calculation of trenches are handled by the open source actinia cloud geoprocessing engine. Actinia is a new OSGeo community project, offering a REST API to GRASS GIS. The actinia processes are scaled and implemented as a service in the Open Telekom Cloud.

In this talk, we present the handling of large and complex input data in the FTTH-process. Focus is on the usage of actinia, and how actinia supports a core process of the largest German telecommunication provider.

State of GRASS GIS Project: 35 years is nothing!

After 35 years of of continuous development, GRASS GIS comes again with great improvements. Being a community-driven project, it offers geospatial analysis, earth observation, time series processing and visualization. It supports large raster files (billions of cells), vector topology, and coupling with SQL databases.

In our presentation we'll give an overview of the latest improvements. The algorithms for interpolation, solar radiation, water flow, and sediment transport have been parallelized. Experimental features include concave hull, vector algebra, point cloud import, DEM fusion and blending, object-based classification, Sentinel data processing, and spatio-temporal algebra. Furthermore, pest spread and urban growth modeling are now available.

Importantly, Python 3 support has been added. Raster storage now benefits from new ZSTD compression. GRASS GIS supports GDAL up to v2.5 and PROJ up to v6. Easy cloud deployment is offered with ready-to-use docker images and an improved test coverage along with continuous integration.

The code development will move to GitHub, including the issues and source code branches since 1987. A new, modern website is on the way, supported by a crowdfunding campaign.