Every week here at Geo Week News, we have been highlighting some of our favorite stories from around the internet that cover the geospatial, 3D, and AEC industries. Whether it’s a fascinating case study, insights from an industry thought leader, or deep dives into new tools, there is never any shortage of great writing and storytelling in this industry. So, below you can find links to three stories that we loved this week.
Measuring Recovery
David Jones, PS, CFS | American Surveyor
When Hurricane Helene tore through Western North Carolina in September 2024, it washed out 6,000 miles of roads, destroyed over 1,000 bridges, and rendered decades of survey control unreliable overnight. McKim & Creed’s geomatics teams detail how they used a combination of conventional surveying, terrestrial and airborne lidar, hydrographic data, and georeferenced imagery to re-establish the geospatial foundation that recovery depended on across five destroyed bridges, 93 rail repair locations, and a flood-ravaged century-old sewer system.
Making AI-enhanced geospatial intelligence accessible and actionable
Wim van Wegen | GIM International
Colombian geospatial firm Cuatro Conceptos has built XphereLab to solve a problem that has quietly replaced data scarcity as the sector’s biggest headache: making sense of the overwhelming volumes of satellite, drone, sensor, and aerial data now available, by letting any user from a field technician to a local mayor ask questions in plain language and get answers grounded in verified geospatial data. The platform’s key design principle is what it refuses to do, unlike general AI tools, XphereLab won’t generate plausible-sounding responses when the data isn’t there, making it a rare example of AI built specifically for high-stakes land management decisions where a fabricated answer is worse than no answer at all.
Challenger Deep: Mapping the ocean’s deepest point
jnally | Spatial Source
A Japanese research team has produced a new preferred depth estimate of 10,927 metres for the Challenger Deep, but the more revealing finding is what the study exposes about precision at full-ocean depth: processing the exact same multibeam sonar data with five different sound-speed models produced results spanning 18 metres, showing that depth estimates at this scale are inseparable from the oceanographic assumptions behind them. The team has made all raw data, processing workflows, and sound-speed models publicly available, so the 2023 survey can be reanalyzed as better oceanographic data becomes available, rather than treated as a fixed answer to a question that turns out to be more complicated than it looks.
