# 2D Semantic Segmentation: Recent Developments and Future Directions
## 5 Common Data Sets and Evaluation Indicators  
Many data sets are available for semantic segmentation tasks. This paper introduces nine representative and general-purpose image segmentation data sets; namely, CamVid (Figure 12), COCO (Figure 13), BDD (Figure 14), Cityscapes (Figure 15), PASCAL VOC 2012 (Figure 16), SBD (Figure 17), KITTI (Figure 18), Mapillary Vistas 3.0 (Figure 19) and VSPW. We have conducted statistics from seven aspects, including the creation time of the dataset, its application scope, classification categories, dataset size, training set, validation set, and test set, as shown in Table 1. These data sets cover different scenes, lighting conditions, and object categories and can be used to evaluate the performance of different algorithms in various complex situations. Moreover, these data sets have extensive application value and can be used to train and test different types of semantic segmentation models. Figure 12: CamVid data set usage and examples. Figure 13: COCO data set usage and examples. Figure 16: PASCAL VOC 2012 data set usage and examples. Figure 14: Berkeley Deep Drive data set usage and examples. Figure 15: Cityscapes data set usage and examples. Figure 19: Mapillary Vistas data set usage and examples. Figure 17: Semantic Boundaries data set usage and examples. Figure 18: KITTI data set usage and examples. However, these data sets also have limitations. For example, the annotations of some data sets are inaccurate and not complete enough, which may affect the accuracy and robustness of the algorithms. In addition, some data sets have limitations in image size and quantity, which may restrict the application scope of the algorithms. Therefore, when selecting data sets, their advantages and disadvantages must be considered, and reasonable choices and processing must be made. We conducted a statistical analysis of the usage of each data set in articles in over the past five years by using Google Scholar data. **1. The CamVid data set**[96] is a data set of urban road scenes publicly released by the University of Cambridge. The data set includes more than 700 accurately annotated images that can be used for supervised learning. The images can be divided into training, validation, and test sets. Meanwhile, 11 commonly used categories are employed in the CamVid data set to evaluate segmentation accuracy. CamVid is the first collection of videos with semantic labels of target categories, and in most cases it can adapt to multiple technical approaches [97]. The CamVid data set is one of the most advanced data sets in real-time semantic segmentation [98]. Its image quality is high, and most images were taken under different sunlight, weather, and seasonal conditions. Hence, the CamVid data set is suitable for studying factors that affect semantic segmentation performance. In addition, the annotations of the CamVid data set are detailed, with each pixel accurately labeled with its corresponding category. The data set is thus suitable for supervised learning, especially for deep learning methods that require precise pixel-level labels. **2. The COCO data set**[99] is a large-scale image data set and one of the most challenging instance segmentation data sets in the field of image processing. This data set is widely used for various image processing tasks including object detection, key point detection, caption generation, and segmentation. The COCO data set contains over 330,000 images, with 220,000 annotated images that include rich semantic information such as 1.5 million objects and 80 object categories. The data set's annotations are rich and of high quality, providing important semantic information and object annotations for the training and evaluation of deep learning models. Additionally, the COCO data set exhibits high quality and diversity in terms of data distribution, complexity, and variability, so it can meet the diverse needs of real-world scenarios. However, the limitations of this data set are evident.