The usefulness of semantic segmentation is described below. However, it turns out that a lot of complex tasks in Vision require this fine-grained understanding of images. For example:
Autonomous driving is one of the complex robotics tasks that requires perception, planning, and execution within constantly evolving environments. This task must be performed with utmost precision since safety is of utter importance. Semantic Segmentation also provides information about free space on the roads, as well as detects lane markings and traffic signs.
Machines can augment analysis performed by radiologists, greatly reducing the time required to run diagnostic tests
For recognizing the type of land cover (e.g., areas of urban, agriculture, water, etc.) for each pixel on a satellite image, land cover classification can be regarded as a multi-class semantic segmentation task. Road and building detection is also an essential research topic for traffic management, city planning, and road monitoring.
Precision farming robots in agriculture can reduce the number of herbicides that are sprayed out in the fields and semantic segmentation of crops and weeds assist them in real-time to trigger weeding actions.