MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641124507 A) filed by Mlr Institute Of Technology; and Marri Laxman Reddy Institute Of Technology And Management on October 17, 2026, for Automated Lunar Crater Detection And Mapping System Using Deep Learning.
Inventors include Mrs. A. Swathi; Mr. Ritesh Biradar; Mrs. Varsha Badda; and Mr. Boddu Venkata Ganesh.
The application for the patent was published on October 30, 2026, under issue no. 44/2026.
Abstract: The present invention relates to an automated lunar crater detection and mapping system using deep learning techniques for identifying and characterizing impact craters on the lunar surface. The system comprises an image acquisition module, a preprocessing module, a terrain feature extraction module, a crater detection module, a crater measurement module, and a geospatial data management module. The image acquisition module receives lunar surface images and, where available, corresponding elevation data from orbital missions and planetary data repositories. The preprocessing module performs image enhancement, radiometric correction, and terrain data normalization to improve the visibility of crater-related features. The crater detection module processes the pre-processed data using a trained deep learning model to identify crater locations and estimate crater boundaries based on morphological, geometric, and textural characteristics. The crater measurement module determines crater coordinates, diameter, and associated confidence scores from the detection results. The generated information is stored in a structured geospatial database for visualization, retrieval, and further analysis. The system supports automated processing of individual lunar images and large image mosaics for lunar surface mapping, terrain characterization, landing-site assessment, and planetary impact analysis. The invention facilitates systematic lunar crater identification and reduces the manual effort involved in processing and analyzing lunar surface data.
Disclaimer: Curated by HT Syndication.
