Revolutionizing Urban Mapping: Deep Learning And Data Fusion Strategies For Accurate Building Footprint Segmentation

Building footprint segmentation is a crucial process for identifying and outlining buildings from aerial or satellite imagery. It has various applications in urban planning, infrastructure management, and disaster response. With the increasing availability of high-resolution imagery, there is a growing need for automated methods for building footprint segmentation. These methods can be broadly categorized into rule-based, machine learning, and deep learning approaches.