| Project Overview | This B.Tech Aerospace / Aeronautical Engineering project is based on the recent research direction 'Graph Neural Networks for Satellite Image Time Series Classification'. The project focuses on applying artificial intelligence, machine learning, deep learning, computer vision, reinforcement learning, surrogate modelling, or RAG-style intelligent assistance to the Satellite and Space Applications area. Students can use the linked 2023-onward research paper/source as the academic base, then convert it into an implementation-focused final-year project with a simplified dataset, simulation model, Python workflow, dashboard, or prototype demonstration. |
|---|---|
| Research Paper Title | Graph Neural Networks for Satellite Image Time Series Classification |
| Research Paper / PDF Link | Open Paper / PDF |
| Year | 2025 |
| Project Area | Satellite and Space Applications |
| Project Type | GNN + Remote Sensing |
| Required Tools / Software | Python, PyTorch/TensorFlow, OpenCV, Rasterio, GeoPandas, Sentinel/Landsat datasets, Streamlit |
| Main Features / Working Principle | Use graph-based learning concepts for satellite time-series classification |
| Expected Output | A time-series classification prototype for land-cover or change detection |
| Possible Add-ons | Add temporal visualization and node importance |
| Get Help | Get Help on WhatsApp
Message: Hi FE, I need help with "Graph Neural Networks for Satellite Image Time Series Classification" in "Aerospace / Aeronautical Engineering" |
This B.Tech aerospace project resource helps students connect a recent AI-based research direction with a practical implementation plan, tools, expected output, and possible extensions.