Semantic segmentation involves labeling each pixel in an image with a class category. This creates a color-coded mask showing exactly which pixels belong to objects like roads, vehicles, buildings, or organs. It's the most detailed form of image annotation used for training high-performance AI models.
• Segmenting organs, cells, tumors, and tissues at pixel level
• Analyzing medical scans for disease progression
• Precise mapping for AI-assisted surgery planning
• Scene understanding: roads, lanes, pedestrians, vehicles, sidewalks
• Pixel-level mapping for LiDAR-camera fusion
• Creating datasets for perception modules
• Shelf-level product segmentation
• Detecting packaging types and shelf space distribution
• Pixel-wise categorization of customer activity zones in smart stores
• Identifying crops, weeds, and soil boundaries
• Drone imagery segmentation for crop health analytics
• Monitoring irrigation zones and disease spread
• Semantic segmentation of buildings, materials, machinery in aerial views
• Site monitoring for equipment and safety zoning
• Identifying risk-prone areas in construction progress maps
• Land cover classification (urban, water, vegetation, etc.)
• Mapping coastlines, roads, and infrastructure from satellite imagery
• Forest fire and environmental change monitoring
