BIT Mesra Women’s Team Builds AI to Map Lunar Craters

A three-woman team at BIT Mesra in Ranchi — Dr. Sanchita Paul, Dr. Mili Ghosh, and Mimansa Sinha — has developed an ISRO-backed AI tool called CraterMorpho that can automatically detect and analyse lunar craters as small as 200 meters, revolutionizing lunar mapping and mission planning, reported regional news daily Jagran.com.

The Jagran article (May 17, 2026) reports that BIT Mesra researchers in Ranchi have developed an AI‑based technology to identify and analyse lunar craters using Chandrayaan‑2 data, aiming to support safer landings and geological studies.

Key Highlights

Photo of Chandrayaan taken with the help of a device developed by scientists of BIT Mesra. [Image - Jagran.com] 

• Team & Leadership: Led by Dr. Sanchita Paul with collaborators Dr. Mili Ghosh and Mimansa Sinha
• Institution: Birla Institute of Technology (BIT) Mesra, Ranchi
• Support: Funded and backed by the Indian Space Research Organisation (ISRO)
• Tool: CraterMorpho — AI system using Digital Elevation Models (DEM)
• Capabilities: Detects craters ≥ 200m, measures depth, roughness, morphology. 

Dr. Sanchita Pal, Dr. Mili Ghosh, Mimansa Sinha [Image - Jagran.com]

In the coming days, there are plans to develop this AI-based system as a fully automated real-time crater analysis pipeline, so that the technology can be fully utilized. Through this, the process of data collection can also be made easier.

Why It Matters

• Mission Safety: Identifies safer lunar landing sites
• Scientific Value: Supports crater dating and geological analysis
• Automation: Replaces manual crater identification
• Global Impact: Strengthens India’s role in AI planetary exploration

Technical Insights

Feature	Details
Data Source	Satellite-derived Digital Elevation Models (DEM)
AI Method	Deep learning neural networks adapted for elevation data
Detection Scale	Craters ≥ 200m diameter
Outputs	Depth, roughness, morphology
Validation	Tested on Aristarchus Plateau, peer-reviewed publications

Challenges Ahead

• Domain Shift: Adapting AI models across orbital sensors
• Extreme Terrain: Handling shadowed regions and overlaps
• Verification: Requires ground truth data from future missions

Context for India

This breakthrough aligns with India’s Space Vision 2047, where AI-driven exploration will play a central role in Moon landings, resource utilization, and habitat planning.