Semantic segmentation is a computer vision technique where each pixel in an image is classified into a predefined category, helping machines understand the context and boundaries of objects in a scene.
🔹 Key Features:
- Pixel-level labeling of images.
- Assigns each pixel to a class (e.g., “car”, “road”, “sky”, “person”).
- Does not differentiate between individual objects of the same class (unlike instance segmentation).
🔹 Use Cases:
Agriculture: Identify crops vs. weeds.
Autonomous vehicles: Understand road scenes by labeling lanes, pedestrians, vehicles, etc.
Medical imaging: Segment organs or tumors in CT/MRI scans.
Satellite imagery: Classify land use (buildings, vegetation, water).