MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641124693 A) filed by S Singaravelan; Dr. M. Sukumar; Dr. J. B. Shajilin Loret; S. Senthilkumar; K. Kalpana; D. J. Jhancy Mabel; Dr. M. Caroline Viola Stella Mary; Bettina Anne Sam; Keren Ranjitha Sam; A. Vinotha Vasuki; Dr. T. Jasperline; and Dr. B. Aravind on October 18, 2026, for Ai-Enabled Multispectral Satellite Imagery And Geospatial Intelligence System For Climate And Environmental Risk Assessment.
Inventors include S Singaravelan; Dr. M. Sukumar; Dr. J. B. Shajilin Loret; S. Senthilkumar; K. Kalpana; D. J. Jhancy Mabel; Dr. M. Caroline Viola Stella Mary; Bettina Anne Sam; Keren Ranjitha Sam; A. Vinotha Vasuki; Dr. T. Jasperline; and Dr. B. Aravind.
The application for the patent was published on October 30, 2026, under issue no. 44/2026.
Abstract: An artificial intelligence (AI)-enabled multispectral satellite imagery and geospatial intelligence system and method for climate and environmental risk assessment are disclosed. The system comprises a distributed ingestion engine configured to acquire multi-temporal, multispectral, and hyperspectral satellite imagery alongside terrestrial sensor data and meteorological telemetry. An automated pre-processing module performs radiometric calibration, atmospheric correction, orthorectification, and cloud-shadow masking. A feature engineering engine extracts biophysical and spectral indices across distinct spectral bands. A deep learning analytics engine comprising a spatio-temporal Convolutional Neural Network (CNN) integrated with a Bidirectional Long Short-Term Memory (Bi-LSTM) network and a Vision Transformer (ViT) processes the extracted indices to identify land-use/land-cover (LULC) dynamics, vegetation degradation, soil moisture deficits, and hydrological anomalies. A geospatial risk modeling engine fuses the extracted features with digital elevation models (DEM) and socio-economic vulnerability layers to generate predictive multi-hazard climate risk heatmaps and real-time alerts. The system improves computational throughput, reduces false-positive anomaly detections, and enables automated, continuous environmental risk profiling.
Disclaimer: Curated by HT Syndication.
