There was a time when remote sensing was mostly about capturing imagery and letting a human analyst figure out what was in it. That time is over. The convergence of machine learning, cloud compute, and sensor technology has turned remote…
GIS & Spatial Data · Fundamentals Geospatial vs Geographic Data: Understanding the Distinction That Shapes Every GIS Workflow, Coordinate Decision, and Spatial Analysis The two terms are used interchangeably across the GIS industry — and that imprecision causes real problems…
Smart Infrastructure · GIS & Spatial Data Geospatial Digital Twins in 2026: How Virtual Representations of the Physical World Are Reshaping Urban Planning, Disaster Management & Environmental Monitoring 3D visualisation, real-time sensor data, open data infrastructure, and OGC standards are…
Smart Infrastructure · AI & Geospatial Analytics Smart Sensors in 2026: The Complete Guide to IoT Sensing Technology for Geospatial, Environmental Monitoring & Smart Infrastructure From temperature monitoring across cold-chain logistics to UWB radar tracking in eldercare, smart sensors are…
The geospatial industry has always been data-heavy. Satellite imagery pipelines, LiDAR point cloud processing, and urban planning models generate volumes of information that have historically required significant manual effort to transform into actionable outputs. But as AI capabilities have matured…
Siemens Bets Big on U.S. Manufacturing to Meet Surging AI Data Centre Demand The expansion of AI workloads is creating massive demand for physical infrastructure — and the companies that build electrical systems are scaling up to meet it. Siemens…
The positioning industry has historically been dominated by satellite-based systems — GPS, GLONASS, Galileo, and BeiDou provide the foundation for most location services worldwide. But satellite positioning has well-known limitations: poor indoor performance, metre-level accuracy in many urban environments, and…
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