Roadmap for 3D Visualization, Contrail Detection, and Image Segmentation

PyVista - 3D Visualization & Mesh Analysis

Renderer: Utilize 3D scene rendering tools like view_isometric().
Surface Normals: Compute mesh shading and lighting.
Point Clouds: Techniques for visualizing point clouds.

Contrail Net - Contrail Detection & Segmentation

Pre-trained ResUNet Model: Leverage for contrail detection.
Image Augmentations: Apply augmentations to enhance model accuracy.
SR Loss using Hough Space: Implement for detection.
Fork from @junzis| contrail-net

Image Segmentation Techniques

Libraries: OpenCV, TensorFlow, Keras, PyTorch, Scikit-Image.
Feature Extraction: Techniques like SIFT, SURF, HOG, LBP.
Graph-Based Segmentation: Focus on RAGs (Region Adjacency Graphs).

Contrail Analysis Roadmap

Data Acquisition and Preparation: Organize and understand data.
Data Quality and Integrity: Perform visual inspection, checks.
Model Building: Implement models for contrail vs. cloud differentiation.
Evaluation and Interpretation: Analyze model results and insights.

Project To-do's

☑︎ Resolve run.py and Dataset Incompatibility.
○ Revise script for modular data transformation and visualization.
□ Organize repository branches.