Building Detection

Building detection from remote sensing imagery focuses on automatically identifying and mapping buildings using satellite or aerial images, aiding urban planning, disaster response, and infrastructure management. Current research emphasizes improving accuracy and robustness through advanced deep learning architectures like transformers (e.g., Swin Transformers) and refined object detection models (e.g., YOLO variants), often incorporating multi-temporal data and contextual information to handle diverse building appearances and challenging conditions. These advancements are crucial for creating more accurate and up-to-date maps of built environments, supporting applications ranging from infrastructure monitoring and disaster assessment to optimizing telecommunications networks and improving humanitarian aid delivery.