Maps for Automated Driving in Railway Domain
We visited the InnoTrans 2026 to get an update about the state of affairs regarding automated driving in railway domain...
“Raumdatenpool” and Other Fancy Data Sources
The general practice of measuring the same areas again and again instead of sharing once-measured data and just updating them as necessary led to an interest in knowing whether some stakeholders got it right and spend money on creating larger data pools...
Simulating Uniquely Modified Objectives
At GEONATIVES we spend some time to improve simulation by making them more realistic and therefore more reliable and spread the word about it. Is it worth to attend conferences which seem to have always similar presentations about hese topics? Yes, it is...
Simulation of Unknown Motion
When it comes to assuring functionality of complex systems, minor causes may have major implications impacting credibility of simulation results. From real-world situations, we would like to postulate that simulation become more realistic in how it resembles individual agents...
The World of Data
On March 30th, 2026, the World Data Organization (WDO) held its inaugural assembly in Beijing China...
The Quiet Rise of Cloud-Native Geospatial Data Formats
A new trend enters the playground for geospatial data exchange, with the potential to disrupt at least some of our common application use cases and workflows...
Experts’ voices: The GIS year in review
We could hardly have wished for a better end to our 2025 series of posts than by (virtually) sitting together with Michael Scholz, research associate and spatial data cruncher at the German Aerospace Center (DLR e.V.) in Braunschweig...
State of Maps
With the advancing of ADAS to ADS, new requirements arise towards map data. Also new tools such as Language Models influence map creation. Let's read the map of map creation...
Niantic Spatial’s Large Geospatial Model
For every problem there is a solution and if we deal with geodata there should be also a spatial intelligence tool. This principle applies to "AI domain" as well and Niantic has a very good foundation for starting a promising enterprise to build a large geospatial model (LGM)...
In Map We Trust
Geospatial Artificial Intelligence can generate as well as check geodata. But which indicators can help to determine if the map is correct or wrong and how useful these indicators are. We also check strategies to work on assessing the trust of maps...