# 2D Semantic Segmentation: Recent Developments and Future Directions
## 5 Common Data Sets and Evaluation Indicators
The data set consists of 21 object categories; the original data set includes 1464, 1449, and 1456 images for training, validation, and testing, respectively. Notably, images in the training and validation sets are pixel-level annotated, which is crucial for scene semantic segmentation. This data set has a high impact and reference value in image segmentation and computer vision because it contains rich scene categories and annotation information, which can effectively support the training and evaluation of scene semantic segmentation and be widely applied in multiple tasks such as image segmentation and object detection in computer vision. Many algorithms and models based on this data set have been proposed, so this data set is of high reference value in academia and industry. One of the limitations of this data set is the small number of object categories, which cannot cover all types of scenes and may thus affect the generalization performance of the model. In addition, the small image size in the data set may have an adverse effect on certain scene semantic segmentation tasks that require high resolution. **6. The Semantic Boundaries data set** (SBD) [103] is commonly used in semantic segmentation tasks to detect boundaries between different objects in an image. The data set was created by researchers at Stanford University and includes images of various indoor and outdoor scenes that can be used to train and evaluate the performance of various semantic segmentation algorithms. The data set is relatively large, with over 20,000 images that are divided into training and testing sets at a ratio of 4:1. The training set contains over 16,000 images, and the testing set contains about 4000 images. The data set can effectively train and evaluate the performance of deep learning models. The limitation of this data set is that it only includes indoor and outdoor scenes and lacks images of some special scenes such as industrial and agricultural production. The imbalanced categories and diversity of scenarios in the SBD data set provide opportunities for researchers to conduct further in-depth research on related issues such as how to solve the imbalanced category problem through data enhancement or an improved loss function as well as how to use multitask learning and other technologies to simultaneously improve the performance of the model in semantic segmentation and boundary detection. **7. The KITTI data set [104]** was jointly created by the Karlsruhe Institute of Technology in Germany and the Toyota Technological Institute at Chicago. It is currently the largest computer vision algorithm evaluation data set for autonomous driving scenes, including urban, rural, and highway scenes. The data set contains a large number of images, point clouds, semantic segmentation annotations, 2D and 3D bounding box annotations, and other data. The image data include both original RGB and grayscale images, with a total of more than 7000 images. The images in the data set are divided into training and test sets, with 3712 images in the training set and 3769 images in the test set. In addition, the data set provides detailed sensor data such as vehicle trajectories, GPS positioning, and LiDAR. The advantage of the KITTI data set lies in its rich scenes and diverse data types, which make the data set suitable for evaluating algorithms for various autonomous driving scenes. In addition, the KITTI data set contains detailed sensor data. It serves as a realistic and rich source of data for algorithm research. However, the KITTI data set has some limitations such as a low image resolution and a relatively small scale, which indicates that it may not cover all scenarios in autonomous driving scenes. **8. Mapillary Vistas 3.0** is a large-scale semantic segmentation data set of urban scenes that includes more than 250,000 high-resolution images and pixel-level semantic segmentation annotations (Figure 20).