Polygonal Building

Polygonal building extraction aims to automatically generate precise vector representations of buildings from aerial or satellite imagery, crucial for accurate mapping and geographic information systems. Recent research focuses on developing end-to-end deep learning models, often employing transformer architectures or region-based convolutional neural networks (R-CNNs), to directly predict building polygons from image data, bypassing inefficient multi-stage pipelines. These advancements improve accuracy and efficiency compared to previous methods, particularly in handling complex building shapes and dense urban areas. The resulting high-quality vector data has significant implications for various applications, including urban planning, disaster response, and infrastructure management.