Urban Scale Dataset

Urban-scale datasets are large-scale collections of urban data used to train and evaluate models for various applications, including urban planning, autonomous driving, and environmental monitoring. Current research focuses on developing datasets with diverse data modalities (e.g., imagery, LiDAR, sensor data) and robust annotations (e.g., semantic segmentation, instance segmentation, attributes), often employing techniques like ensemble learning, neural radiance fields (NeRFs), and large language models (LLMs) to address challenges like occlusion, noise, and domain adaptation. These datasets are crucial for advancing computer vision, machine learning, and urban informatics, enabling more accurate and reliable models for a wide range of smart city applications.