# 2D Semantic Segmentation: Recent Developments and Future Directions
## 1 Introduction  
Semantic segmentation is one of the three major research topics in computer vision and the basis of various complex visual tasks. From a classification perspective, all pixels in semantic segmentation should be assigned label information. Pixel-level semantic segmentation in region-level classification is more challenging than image classification and region-level object detection. The task of semantic segmentation is to assign each pixel of an image to a corresponding class label [1] from a predefined set of classes; that is, semantic segmentation is based on pixel-to-pixel classification of labeled data [2; 3]. The massive success of deep learning has had a great impact on semantic segmentation methods [4; 5; 6] because it has improved their accuracy, and it has attracted the interest of many people in technical and research fields that require computer vision capabilities. Semantic segmentation has been widely used in various fields because it can provide scene understanding at the pixel level. For example, effective extraction of traffic road networks from satellite images is essential in car navigation and road planning [7]. In robotics and surveillance, temporary localization and classification of action clips are particularly important [8]. Meanwhile, in unmanned driving, semantic segmentation is one of the key technologies for road-scene understanding [9; 10; 11; 12], and it guarantees the safe driving of autonomous vehicles. In addition, semantic segmentation has certain applications in pedestrian detection [13; 14], remote sensing [15; 16], pose estimation [17; 18], and the medical field [19; 20; 21; 22].  
However, despite important progress in recent years, pixel-level semantic segmentation still faces many challenges, especially when dealing with complex scenarios and varying environmental conditions.  
We chose this specific subject because we recognize the importance of having a deep understanding of the current techniques and challenges in image semantic segmentation and the direction of future development. Furthermore, despite the multitude of studies on image semantic segmentation, a comprehensive, systematic overview of recent advancements and emerging technologies remains lacking. This situation makes understanding the research progress in this field difficult, especially for those new to this area.  
Centering on the key technical methods that emerged during the development of image semantic segmentation, this study expounds upon the evolution from traditional methods to deep learning and discusses the strengths and weaknesses of each method and the directions that may be explored further in the future. An appropriate analysis is conducted on some commonly used and challenging data sets along with their evaluation metrics. We hope that this review will provide readers a clear global perspective toward understanding existing challenges and potential future solutions.  
The remainder of this paper is organized as follows. Section 2 discusses some traditional methods that preceded deep learning. Section 3 provides a qualitative analysis of existing deep learning approaches. Section 4 describes the key technological developments in deep learning for semantic segmentation and the challenges at this stage. It also provides a detailed analysis of current issues and identifies future research directions. Section 5 briefly introduces common data sets and evaluation metrics used in the field, and Section 6 discusses the current challenges in semantic segmentation and the potential research directions.