Semantic Segmentation in Satellite Imagery

Overview

Explore advanced techniques in deep learning for image segmentation and object detection, focusing on practical applications in autonomous driving.

Syllabus

• Course Introduction
Overview of Semantic Segmentation
Importance in Satellite Imagery and Autonomous Driving
• Fundamentals of Image Segmentation
Introduction to Image Segmentation Techniques
Classical Methods vs. Deep Learning Approaches
• Deep Learning Basics
Neural Networks Overview
Convolutional Neural Networks (CNNs) Essentials
• Advanced Semantic Segmentation Techniques
Fully Convolutional Networks (FCNs)
U-Net Architecture
DeepLab Variants
• Data Preparation and Annotation
Satellite Imagery Datasets
Image Annotation Tools and Techniques
Data Augmentation for Segmentation Models
• Model Training and Optimization
Training Strategies and Best Practices
Loss Functions for Segmentation
Hyperparameter Tuning
• Evaluation Metrics
Intersection over Union (IoU)
Precision, Recall, and F1 Score
Model Evaluation in Real-world Scenarios
• Object Detection in Satellite Imagery
Introduction to Object Detection
YOLO and Faster R-CNN Architectures
Integrating Segmentation and Detection
• Applications in Autonomous Driving
Semantic Segmentation for Navigation
Detecting Vehicles, Pedestrians, and Obstacles
Challenges and Solutions in Real-time Processing
• Practical Implementation
Tools and Frameworks (e.g., TensorFlow, PyTorch)
Building a Segmentation Model from Scratch
Deploying Models on Edge Devices
• Case Studies and Industry Applications
Real-world Projects and Companies
Future Trends in Semantic Segmentation
• Capstone Project
Designing and Implementing Your Own Segmentation Solution
Presentation and Peer Review
• Course Summary and Next Steps
Recap of Key Concepts
Resources for Further Learning

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Subjects

Data Science