To provide participants with a deep understanding of the U-Net architecture, its relevance to semantic segmentation tasks, and its application for RTS mapping.
Breakdown
Introduction to Semantic Segmentation
Definition and significance of semantic segmentation
Differences between classification, object detection, and segmentation
Relevance to RTS mapping
Overview of U-Net Architecture
Historical context: Why and where was U-Net developed?
Key features of U-Net: Symmetry, skip connections, etc.
Visual representation of U-Net's architecture
Essentials of U-Net Components
Contracting path and its role in feature extraction
Bottleneck: Capturing the context
Expansive path: Localizing features using skip connections
Introduction to Other Semantic Segmentation Models
FCN (Fully Convolutional Network): The pioneer in end-to-end segmentation
SegNet: Architecture with encoder-decoder structure
...
Case Study: U-Net for RTS Mapping
Walkthrough of a real-world application of U-Net for RTS mapping
Deep dive into U-Net for semantic segmentation
Goal
To provide participants with a deep understanding of the U-Net architecture, its relevance to semantic segmentation tasks, and its application for RTS mapping.
Breakdown