Week 1: Introduction to Satellite Imagery

• Overview of satellite imaging
• Types of satellite images (e.g., multispectral, hyperspectral, radar)
• Introduction to Sentinel satellite data
• Applications of satellite imagery (e.g., environmental monitoring, urban planning, disaster management)

Week 2: Basics of Remote Sensing

• Fundamentals of remote sensing
• Electromagnetic spectrum and its relevance to remote sensing
• Image acquisition and preprocessing
• Tools and software for remote sensing (e.g., QGIS, SNAP)

Week 3: Understanding Raster Data

• Introduction to raster data and its structure
• Raster data processing techniques
• Handling and visualization of raster data using Python libraries (e.g., GDAL, rasterio)

Week 4: Data Acquisition and Preprocessing

• Accessing Sentinel data (e.g., Copernicus Open Access Hub, Google Earth Engine)
• Data preprocessing steps (e.g., atmospheric correction, geometric correction, resampling)
• Handling missing data and noise

Week 5: Introduction to Deep Learning

• Basics of deep learning and neural networks
• Overview of popular deep learning frameworks (e.g., TensorFlow, PyTorch)
• Setting up the environment for deep learning

Week 6: Convolutional Neural Networks (CNNs)

• Introduction to CNNs and their architecture
• Key operations in CNNs (e.g., convolution, pooling, activation functions)
• Implementing a simple CNN for image classification

Week 7: Advanced CNN Architectures

• Deep CNN architectures (e.g., VGG, ResNet, Inception)
• Transfer learning and fine-tuning pre-trained models
• Practical implementation using Sentinel data

Week 8: Image Segmentation Techniques

• Introduction to image segmentation
• Techniques for image segmentation (e.g., U-Net, SegNet, Mask R-CNN)
• Practical implementation of segmentation models on satellite imagery

Week 9: Object Detection in Satellite Images

• Introduction to object detection
• Object detection algorithms (e.g., YOLO, SSD, Faster R-CNN)
• Implementing object detection on satellite images

Week 10: Change Detection and Time Series Analysis

• Introduction to change detection
• Techniques for change detection in satellite images
• Time series analysis using satellite imagery

Week 11: Land Use and Land Cover Classification

• Overview of land use and land cover (LULC) classification
• Techniques for LULC classification using deep learning
• Practical implementation using Sentinel data

Week 12: Evaluation and Validation

• Evaluation metrics for image classification, segmentation, and detection
• Techniques for model validation and cross-validation
• Practical tips for improving model performance

Week 13: Case Studies and Applications

• Case studies of deep learning applications in remote sensing
• Real-world projects using Sentinel data
• Discussion of challenges and future trends in satellite image analysis

Recommended Resources

• Books: "Deep Learning for Remote Sensing Image Classification" by Gustau Camps-Valls et al.
• Online Courses: "Satellite Imagery Analysis with Python" on Coursera
• Research Papers: Recent publications in journals like IEEE Transactions on Geoscience and Remote Sensing