Roof Type Data

Roof type data research focuses on automatically extracting and classifying roof characteristics from various data sources, primarily aerial imagery and LiDAR, to support applications ranging from disaster risk assessment to urban planning. Current research employs deep learning models, including convolutional neural networks (CNNs), U-Nets, and transformer architectures like PolyGen, often incorporating multi-modal data fusion techniques to improve accuracy and robustness. These advancements enable more efficient and accurate generation of high-resolution roof type datasets, which are crucial for improving building information models and informing analyses related to public health, sustainability, and infrastructure resilience.