Lunar Hazard Mapper, developed as part of the Smart India Hackathon (SIH) 2023, stands as a project that baged 4th place dedicated to ensuring the safety of lunar landers. The core focus lies in generating hazard maps using advanced image analysis techniques and deep learning models. The project aims to identify potential threats on the lunar surface, thereby enhancing safety protocols for lunar missions.
- Image Analysis: Advanced image processing techniques employed to analyze Terrain Mapping Camera (TMC) data.
- Hazard Identification: Detection and analysis of shadows, slopes, and key surface features critical for identifying potential threats.
- Object Detection: Implementation of deep learning models for recognizing critical elements like craters and boulders on the lunar surface.
- Hazard Map Generation: Fusion of various analysis outputs to generate detailed hazard maps essential for the safe navigation of lunar landers.
- Python: Primary language for image processing, deep learning model development, and data analysis.
- Libraries: OpenCV, Matplotlib, NumPy, TensorFlow, PyTorch, Rasterio, among others used for various tasks.
- Algorithms: Edge detection, object detection, and image segmentation algorithms employed for comprehensive analysis.
- Data Handling: COCO format and custom data handling techniques used for model training and evaluation.
- Saket Gudimella
- Kishore Ravishankar
- Surya Narayanan
- Tharun Iyer
- Aarick Yadhuvir
- Suchi Arora
This project is licensed under the MIT License - see the LICENSE file for details.