image segmentation Semantic Segmentation Dataset and Pre-Trained Model by image segmentation

Images

433 images

Explore Dataset

Here are a few use cases for this project:

Traffic Management: This model could be used by traffic management authorities to analyze current road patterns and help optimize traffic flow. They could identify roads and their types in different areas, which would be crucial for implementing traffic signals, determining road capacities and planning traffic routes.
Autonomous Vehicles: Autonomous vehicles use computer vision technology constantly, key among them being the recognition of paths and roadways. With this image segmentation model, self-driving cars could accurately identify left roads, right roads, and U-turn roads, enhancing their navigation capacities and safety.
City Planning: Urban and city planners could use this technology to assess the infrastructure of both existing and developing cities. By identifying the types and layouts of roads, they can make better decisions about zoning, infrastructure development and renovation.
Virtual Map Development: Companies like Google, Apple, and Microsoft, which provide map services, could leverage the model to provide more detailed and accurate road information on their maps. This can improve user navigation experience.
Traffic Simulation Games: Game developers could use this image segmentation model in the development of hyper-realistic traffic or city simulation games. It could be used to create virtual environments with realistic road structures.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            image-segmentation-vqwee_dataset,
                            title = { image segmentation Dataset },
                            type = { Open Source Dataset },
                            author = { image segmentation },
                            howpublished = { \url{ https://universe.roboflow.com/image-segmentation-awhml/image-segmentation-vqwee } },
                            url = { https://universe.roboflow.com/image-segmentation-awhml/image-segmentation-vqwee },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { feb },
                            note = { visited on 2024-06-26 },
                            }