LunaVision: Reimagining Lunar Terrain with AI

High-Resolution DEMs from Chandrayaan Imagery using Shape-from-Shading (SfS)

Explore Our Mission

🔍 Concept Overview

What are DEMs?

Digital Elevation Models (DEMs) are 3D representations of terrain surfaces. They provide precise height information for every point on the lunar surface, essential for mission planning and scientific analysis.

Shape-from-Shading

Shape-from-Shading (SfS) is a computer vision technique that reconstructs 3D surfaces by analyzing how light interacts with objects in 2D images, extracting height information from shadow patterns.

Key Benefits

SfS enables high-resolution terrain mapping from single images, overcoming limitations of traditional methods like LIDAR and stereo imaging, which require multiple data sources or specialized equipment.

📸 Why Shape-from-Shading?

Traditional Methods

  • Requires multiple images (stereo)
  • Expensive specialized equipment (LIDAR)
  • Lower resolution in many cases
  • Limited by orbital constraints
  • Higher computational requirements

SfS Advantages

  • Works with single images
  • Uses existing camera systems
  • Higher resolution possible
  • More flexible imaging conditions
  • AI-optimized processing

🔧 How It Works

1

Input

High-resolution lunar imagery from Chandrayaan-2 OHRC or other orbital cameras

2

Preprocessing

Image enhancement, noise reduction, and normalization for optimal analysis

3

Photometric Modeling

Analysis of light and shadow patterns to determine surface properties

4

Shape Reconstruction

AI-powered algorithms convert light patterns into precise elevation data

5

Output

High-resolution DEM with enhanced detail for mission planning and analysis

📈 Applications

Lunar Missions

Precise landing site selection and hazard avoidance for future lunar missions

NASA/ISRO Use

Integration with existing mission planning tools for Chandrayaan and Artemis programs

AI + Remote Sensing

Advanced terrain analysis combining machine learning with traditional remote sensing

Rover Planning

Optimized path planning and navigation for lunar rovers in challenging terrain

💡 Unique Selling Points

AI-Powered DEM Generation

Advanced neural networks optimize the Shape-from-Shading process, delivering higher accuracy and resolution than traditional methods.

Integrated Boulder Detection

Automatically identifies and maps hazardous surface features like boulders and craters for safer mission planning.

Visual-first Approach

Prioritizes visual clarity and intuitive representation of terrain data for easier interpretation by mission planners.

Modularity

Flexible architecture allows integration with existing workflows and tools, enhancing rather than replacing current systems.

Multi-purpose Output

Generates data in multiple formats suitable for various applications from scientific analysis to 3D printing of terrain models.

🚀 Tech Stack & Architecture

Core AI Stack
Imaging & Preprocessing
Visualization & UX
AI Enhancements
Dev & Deployment
Python
PyTorch
TensorFlow
NumPy
SciPy
OpenCV
PIL/Pillow
Scikit-image
GDAL
Rasterio
CesiumJS
QGIS
Blender
Matplotlib
Plotly
U-Net
GAN
YOLO
CUDA
TensorRT
Docker
GitHub
Flask/FastAPI
PostgreSQL
AWS/Azure

📂 Dataset

Chandrayaan-2 OHRC/TMC

High-resolution imagery from India's lunar mission with exceptional detail of the lunar surface

Access Data

LRO NAC/WAC

NASA's Lunar Reconnaissance Orbiter camera data providing comprehensive lunar surface coverage

Access Data

Kaguya Terrain

Japanese SELENE mission data with detailed topographic measurements of the lunar surface

Access Data

📊 Output Formats

.tif

GeoTIFF format for GIS compatibility

.asc

ASCII grid format for wide compatibility

.stl

3D model format for printing and visualization

Contours

Vector contour lines for traditional mapping

🚀 Scalability & Future Vision

Stereo + LiDAR Fusion

Integrating multiple data sources for even higher accuracy and resolution

Boulder Detection Models

Advanced AI for automatic identification of surface hazards

Simulation for Rovers

Real-time terrain simulation for rover navigation training

Mars Extension

Adapting the technology for Mars surface mapping

💰 Budget

Prototype

0

Initial proof-of-concept development

Real Integration

0

Full-scale implementation with ISRO systems

📽 Visual Showcase

📬 Contact

hackerunity.community@gmail.com

github.com/Adityadahuja/LunaVision-2.0

Bhartiya Antariksh Hackathon 2025