Building Extraction

Building extraction, the automated identification of buildings from remote sensing imagery (like satellite or aerial photos), aims to create accurate and up-to-date building maps for various applications. Current research emphasizes improving the accuracy and efficiency of extraction, focusing on deep learning models such as U-Net++, Vision Transformers, and other advanced architectures, often incorporating techniques like multi-task learning and data augmentation to handle diverse building styles and data scarcity. These advancements are crucial for urban planning, disaster response, and economic analysis, enabling more precise and timely information about built environments globally. The development of large, diverse datasets is also a key focus to improve model generalization and robustness.