Enhancing Planetary Surface Analysis using a Hybrid Deep Learning Framework: Combining CNN-Based Image Classification with LLM-Driven Semantic Explanations

Sony Annem

Title:
Enhancing Planetary Surface Analysis using a Hybrid Deep Learning Framework: Combining CNN-Based Image Classification with LLM-Driven Semantic Explanations

Authors:
Sony Annem

Cite This Article :

Sony Annem "Enhancing Planetary Surface Analysis using a Hybrid Deep Learning Framework: Combining CNN-Based Image Classification with LLM-Driven Semantic Explanations" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-3, June 2025, pp.708-714, URL: https://www.ijtsrd.com/papers/ijtsrd79929.pdf

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Abstract :
Accurate identification and interpretation of planetary surface features are critical for advancing extraterrestrial geological research. In this study, we propose a two-stage intelligent framework that combines a Convolutional Neural Network (CNN) with a Large Language Model (LLM) to classify and explain features on the Martian surface. CNN is trained on annotated satellite imagery from NASA’s HiRISE dataset to detect surface structures such as craters, dunes, slope streaks, and impact ejecta. Once a class is predicted, the output is passed to a state-of-the-art LLM (Meta’s LLaMA-4 model accessed via Groq API), which generates human-readable scientific explanations for the detected features. Our system not only identifies features accurately but also explains them clearly. This shows how AI can help scientists better understand planets and could be useful for future space missions.

Keywords :
AI for Space Research

Publication Details:

Unique Identification Number : IJTSRD79929

Published In : Volume-9 | Issue-3, June 2025

Page Number(s) : 708-714

Publisher Name : IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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