MUMBAI, India, Oct. 5 -- Intellectual Property India has published a patent application (202641115340 A) filed by Rameshbabu R on September 26, 2026, for An Intelligent Natural Language Platform For Evidence-Grounded Multi-Temporal And Multi- Sensor Satellite Image Analysis.
Inventors include Rameshbabu R; Malarvizhi T; Angu Abishek M; Dineshkumar M; Tharun M; Charrunethra A P; Thaanya S; and Sriranjani N.
The application for the patent was published on October 02, 2026, under issue no. 40/2026.
Abstract: Satellite imagery carries substantial information about land cover, infrastructure, water bodies, vegetation, environmental change, and surface conditions, yet extracting this information typically requires domain-specific image-processing expertise and several independent analytical tools. The present invention, SatQuery AI, provides an intelligent satellite-image analysis system that allows a user to interrogate satellite imagery through natural-language queries. The system accepts optical, synthetic aperture radar (SAR), or temporally paired (bi-temporal) satellite observations and automatically determines the analytical task called for by the user's query. A task- oriented processing architecture selects the geospatial and computer-vision analysis modules appropriate to that task, covering operations such as water detection, vegetation assessment, built-up-area analysis, object detection, optical-SAR comparison, and bi-temporal change detection. The resulting measurements are organised into a structured evidence representation before being passed to an artificial-intelligence reasoning layer. A validation stage checks that the generated response remains consistent with the extracted evidence, reducing the risk of unsupported or fabricated observations. For bi-temporal imagery, the system identifies spatial and land-cover changes between two observations and derives an evidence-based future-trend assessment together with a recommended monitoring action. For optical-SAR analysis, the two sensing modalities are processed independently and combined into an evidence-grounded comparison. By bringing together natural-language interaction, automated task routing, geospatial processing, computer vision, multi-sensor analysis, temporal change detection, structured evidence representation, artificial- intelligence reasoning, and response validation within a single workflow, the system provides an explainable and accessible approach to satellite-image interpretation, without requiring the user to manually select individual remote-sensing processing operations.
Disclaimer: Curated by HT Syndication.
