Building Footprint

Building footprint extraction, the process of identifying and mapping the outlines of buildings, is crucial for urban planning, disaster response, and environmental monitoring. Current research focuses on improving accuracy and efficiency using various deep learning architectures, including convolutional neural networks (CNNs), generative adversarial networks (GANs), and diffusion models, often leveraging multimodal data sources like satellite imagery, LiDAR, and volunteered geographic information (VGI). These advancements are driven by the need for more precise and readily available building data, particularly in developing countries and for historical analysis, impacting fields ranging from urban development to climate change research. The development of large, open-access datasets is also a significant focus, enabling broader collaboration and more robust model training.