Fine Grained Building

Fine-grained building analysis focuses on extracting detailed information about individual buildings from various image sources, such as satellite and street-view imagery, aiming for precise segmentation, functional classification, and attribute recognition. Current research heavily utilizes deep learning, employing architectures like Vision Transformers and convolutional neural networks, often incorporating semi-supervised learning and techniques to address challenges posed by data scarcity, resolution limitations, and diverse building styles. These advancements are crucial for improving urban planning, infrastructure management, and renewable energy resource assessment by providing accurate, large-scale building datasets and models for various applications.