This Course combines AI, Machine Learning, and Satellite Data for Disaster Risk Assessment and Monitoring.
Note: Make sure to like the Repo if you are gonna copy the solution :D helps me out a ton
- Satellite Imagery: Learn to process optical and radar satellite data to monitor and assess natural disasters like floods, hurricanes, and wildfires.
- Disaster Monitoring: Use satellite imagery for real-time damage assessment and integrate it into early warning systems.
- AI & Machine Learning: Apply computer vision techniques (e.g., CNNs) for image classification, segmentation, and disaster risk prediction.
- Geospatial Data Analysis: Utilize GIS tools to combine satellite imagery with spatial data to create disaster risk maps.
- Cloud & GPU Computing: Leverage Nvidia’s GPU solutions and cloud platforms for large-scale data processing.
- Disaster Risk Assessment: Develop models to predict the severity of disasters using satellite data.
- Applications: Implement disaster monitoring, damage assessment, and environmental protection strategies.
- Ethical Considerations: Address data privacy, AI bias, and ethical issues in satellite data usage.
- AI & ML Frameworks: TensorFlow, PyTorch
- Geospatial Tools: GeoPandas, rasterio, GIS platforms
- Cloud Platforms: Nvidia DGX, Google Cloud, AWS
- Visualization: Matplotlib, Plotly
- Nvidia DALI: Accelerate data loading and preprocessing for faster model training.
- TAO Toolkit: Leverage pre-trained models and perform transfer learning for rapid deployment.
- Transfer Learning: Fine-tune pre-trained models on satellite imagery to improve performance on specific disaster-related tasks.
- Nvidia Triton: Deploy AI models at scale with Nvidia Triton for optimized inference and scalability.
- Disaster Prediction: Use satellite data to predict disaster events like floods or wildfires based on environmental conditions and historical data.
- Damage Assessment: Post-disaster satellite imagery analysis to assess the scale of damage, aiding recovery efforts and resource allocation.
- Risk Mapping: Generate real-time risk maps to identify areas at high risk for future disasters, improving emergency planning.
- Early Warning Systems: Integrate satellite data with AI models to trigger alerts for potential disasters like hurricanes or tsunamis.
- Environmental Monitoring: Track environmental changes such as deforestation, urban expansion, or soil degradation that can contribute to disaster risks.
- Humanitarian Aid: Assist NGOs and governments in coordinating humanitarian aid by providing precise, real-time data on affected areas.
- Urban Planning & Resilience: Support smarter city planning by predicting disaster-prone areas and improving infrastructure resilience.