Semantic segmentation of urban environments: Leveraging U-Net deep learning model for cityscape image analysis.

ABSTRACT: Semantic segmentation of cityscapes via deep learning is an essential and game-changing research topic that offers a more nuanced comprehension of urban landscapes. Deep learning techniques tackle urban complexity and diversity, which unlocks a broad range of applications. These include urban planning, transportation management, autonomous driving, and smart city efforts. Through rich context and insights, semantic segmentation helps decision-makers and stakeholders make educated decisions for sustainable and effective urban development. This study investigates an in-depth exploration of cityscape image segmentation using the U-Net deep learning model. The proposed U-Net architecture comprises an encoder and decoder structure. The encoder uses convolutional layers and down sampling to extr

SUBMITTER: Arulananth TS 

PROVIDER: S-EPMC10997063 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature