KITTI-Road-Segmentation

This dataset is designed to facilitate the development, implementation, and testing of various segmentation architectures and techniques for road segmentation tasks. It serves as an ideal resource for researchers and practitioners in the fields of autonomous driving, computer vision, and deep learning.

Dataset Contents

• Images: High-quality images of roads from real-world driving scenarios.
• Segmentation Masks: Ground truth masks indicating road regions for precise segmentation model training and evaluation.

Applications

• Autonomous vehicle navigation and road understanding.
• Development and benchmarking of segmentation models.
• Computer vision research in outdoor and urban environments.

Data Sources

This dataset is curated from reliable, widely recognized sources to ensure quality and usability:

1. KITTI Vision Benchmark Suite: Renowned for its high-resolution data tailored for computer vision research.
2. Udacity Self-Driving Cars Nanodegree Projects: Includes data from Projects 1 and 2, specifically aimed at advancing self-driving car technologies.

Key Features

• Real-world driving data for robust model training.
• Compatible with popular segmentation frameworks such as UNet, DeepLab, and Mask R-CNN.
• Supports deep learning applications like road detection, traffic scene analysis, and autonomous vehicle simulation.