Thermal and Morphological Analysis of Lunar Lava Flows Using Machine Learning and Stefan-Boltzmann Modeling
ISEF · 2026 Physics and Astronomy
Overview
The objective of this research was to develop a dual-methodology framework for the autonomous identification and physical characterization of lunar lava flows using multi-modal orbital data. While current NASA initiatives emphasize human-led "citizen science," this project proposes a scalable computational alternative. A Convolutional Neural Network (CNN) was engineered to classify lunar morphology using high-resolution imagery from the Lunar Reconnaissance Orbiter Camera (LROC). A dataset of 1,000 tiles was curated from three distinct volcanic regions: Hadley Rille, Archimedes Crater, and Mare Serenitatis. The model utilized a 70/15/15 split for training, validation, and testing. To transition from visual classification to physical analysis, topographic profiles were extracted from the Lunar Orbiter Laser Altimeter (LOLA) to determine flow thickness via elevation differential analysis. These measurements were integrated into a physics-based thermal model utilizing the Stefan-Boltzmann Law to simulate radiative cooling durations under lunar environmental constraints. The CNN achieved a classification accuracy of 94.2% (Precision of 0.91, Recall of 0.86, F1-score of 0.88), demonstrating high reliability in distinguishing basaltic flows from complex background terrain. Thermal modeling revealed a strong linear positive correlation between flow thickness and radiative cooling duration, providing a quantitative method for estimating the heat-transfer history of identified flows. This integrated approach demonstrates that combining deep learning with thermophysical constraints significantly enhances the utility of automated planetary mapping, offering a scalable, robust tool for Artemis landing site selection and the geological characterization of the lunar surface.
Competition history
- ISEF 2026
Resources
Related projects
ISEF · 2022
A Novel Object Detection-Based Method To Detect Craters and Rilles on the Lunar Surface
ISEF · 2026
DeepFlare: Solar Flare Forecasting and Active Region Monitoring Using Multi-Modal Machine Learning
ISEF · 2025
A Comprehensive Machine Learning Paradigm for Space Debris Surveillance: An Integrated Triple Model Framework for Identification, Orbital Prediction, and Collision Risk Evaluation
ISEF · 2017
A Linked Learning Approach to Automated Galaxy Morphology Classification
Closest projects by meaning, across every fair and year in the corpus.
Source: Regeneron International Science and Engineering Fair