Publication2026
Crater Morph: Automated Lunar Crater Detection
The record
- Type
- Publication
- Date
- 1 January 2026
- Level
- National
- Journal
- International Conference on Connected Intelligence for Industrial Applications Ci2a 2026
- Published
- 1 January 2026
- DOI
- 10.1109/ci2a69097.2026.11576820
- Scopus ID
- 105044959516
- Authors
- 6
- Open access
- No
- Citations
- 0
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Deep learning-based lunar crater detection using Chandrayaan-2 TMC-2 imagery and YOLOv5 models advances space technology research and planetary surface analysis capabilities.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-3.5 Space, defence and strategic technology
Lunar surface analysis using space mission data and advanced artificial intelligence represents space technology and strategic technology advancement.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “Crater Morph: Automated Lunar Crater Detection”. Kortex.