Footprint Extraction

Footprint extraction encompasses the automated identification and delineation of objects' boundaries from various data sources, aiming for accurate and efficient mapping. Current research focuses on improving the accuracy and efficiency of footprint extraction using deep learning models, such as convolutional neural networks (CNNs) and graph convolutional networks (GCNs), often incorporating techniques like semantic segmentation, self-supervised learning, and multi-level supervision to handle diverse data types (e.g., satellite imagery, LiDAR). These advancements have significant implications for various fields, including urban planning, environmental monitoring, and autonomous navigation, by enabling cost-effective and large-scale mapping of features like buildings, linear disturbances, and animal tracks.