Building Change Detection

Building change detection, the automated identification of alterations in building structures over time using remote sensing data, aims to improve urban monitoring and mapping. Current research emphasizes deep learning approaches, particularly encoder-decoder architectures and contrastive learning methods, to analyze both multi-temporal imagery and LiDAR data, often incorporating auxiliary tasks to enhance accuracy and address challenges like off-nadir viewing angles and intra-class changes. These advancements are crucial for efficient urban planning, disaster response, and the maintenance of accurate building databases, impacting various fields from urban studies to environmental monitoring.