# 2D Semantic Segmentation: Recent Developments and Future Directions
## 5 Common Data Sets and Evaluation Indicators
First, the data set contains relatively few object categories, so the needs of specific domains or tasks cannot be easily met. Second, the images in the data set are mostly real images from natural scenes and cannot fully satisfy the demands of special scenarios. **3. The Berkeley Deep Drive (BDD) data set**[100], a detailed data set of autonomous driving urban scenes, includes tasks such as object detection, multiobject tracking, semantic segmentation, instance segmentation, segmentation tracking, and lane detection. The BDD data set has 19 categories for semantic segmentation, but its application is limited due to its \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data Set** & **Time** & \begin{tabular}{c} **Application** \\ **Scenario** \\ \end{tabular} & **Classification** & **Quantity** & \begin{tabular}{c} **Number of** \\ **Training Sets** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Validation Sets** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Test Sets** \\ \end{tabular} \\ \hline CamVid & 2008 & City street & 32 & 700+ & 367 & 100 & 233 \\ COCO & 2014 & Multiple & 81 & 308,000 & 82,783 & 40,504 & 81,434 \\ BDD & 2018 & City street & 19 & 100,000 & 70,000 & — & 30,000 \\ Cityscapes & 2016 & City street & 30 & 5000 & 2975 & 500 & 1525 \\ PASCAL-VOC 2012 & 2015 & Multiple & 21 & 9993 & 1464 & 1449 & 1452 \\ SBD & 2014 & Multiple & 21 & — & 8498 & 2857 & — \\ KITTI & 2015 & Multiple & 7 & 7000+ & 3712 & — & 3769 \\ Mapillary Vistas 3. 0 & 2021 & City scenarios & 66 & 250 K & 180 K & 20 K & 30 K \\ VSPW & 2021 & City scenarios & 124 & 3537 (video) & — & — & — \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Popular semantic segmentation data sets. samples not being practical for semantic segmentation of urban scenes. Specifically, the BDD data set contains approximately 100,000 images, among which about 70,000 are used for training and about 30,000 are used for testing. The advantage of this data set lies in its detailed urban scene data, which can be applied to multiple autonomous driving scene tasks. In addition, the multitask nature of the data set makes it highly valuable for research and application in the field of autonomous driving. The application of the BDD data set in semantic segmentation is limited because its samples are not practical for use in the semantic segmentation of urban scenes, which may affect its practicality in certain scenarios. In addition, the data set is large in scale and requires a large amount of computational resources for data processing and model training, which may pose challenges to research and applications. **4. The Cityscapes data set [101]** is a popular data set for autonomous driving tasks aimed at object recognition and localization within a scene. The data set comprises a large collection of high-resolution street-level images from 50 different cities, including 5000 annotated images with pixel-level annotation for 19 object categories. To facilitate model training and testing, the data set is divided into three subsets: 2975 training images, 500 validation images, and 1525 test images. The benefits of using this data set include the large number of high-resolution images with pixel-level annotation that can effectively help models learn object recognition and localization. Additionally, because the data set covers scenes from 50 different cities, it effectively encompasses various scenes and traffic conditions, making the model highly generalizable. However, the data set's large size requires a large amount of computing resources and storage space, so it may not be suitable for some scenarios with limited resources. Furthermore, the annotations in the data set may contain errors and require certain data cleaning and processing to ensure the reliability of the model's training and testing results. **5. PASCAL VOC 2012 [102]** is a widely used data set for scene semantic segmentation that provides labeled data for supervised learning in visual tasks.