The Mantra for Geospatial AI: Trust, but Verify!
Dr. Mukund Kadursrinivas Rao
As we shift toward automated decision-making, AI “magic box” must be a highly audited pipeline. There are four important structural pillars - Explainable AI (XAI) & Semantic Clarity to explain what and which data (pixels or spatial features or text data) influenced the AI model’s decision; Semantic Layering as a “Guardrail” that ensures the AI isn’t just matching patterns, but is following human-like logic (e.g., “A building would not exist in the middle of a permanent water body” – verify such outcomes!) and AI Model Cards, which are like a nutrition label, or Metadata documenting its Geo FM data, Training data, known biases, math, reasoning and logic and recommended use cases...[more]

GNSS Constellation Specific Monthly Analysis Summary: December 2025
Narayan Dhital
This article continues the monthly performance analysis of the GNSS constellation. Readers are encouraged to refer to previous issues for foundational discussions and earlier results. In addition, there is a short update on the India’s regional navigation systems in the performance remarks section... [more]

Is it possible to generate accurate 3D point clouds with UAS-LIDAR and UAS-RGB photogrammetry without GCPs?
Álvaro Gómez-Gutiérrez, Manuel Sánchez-Fernández, José Juan de Sanjosé-Blasco, Napoleón Gudino-Elizondo and Francisco Lavado-Contador
The UAS-LIDAR and UAS- RGB with PPK produced unbiased point clouds, being the latter the most cost- effective method. For the other direct geo-referencing systems/approaches, the acquisition of GCP or the co-registration of the resulting point cloud is still necessary... [more]