Accurate, topologically consistent building footprints are essential for building reconstruction and GIS applications. But high-resolution orthophotos often contain occlusions (trees, cast shadows, etc.) or dense roof structures that challenge pixel-based segmentation and polygonization. In recent years, Line Segment Detection (LSD) networks have gained popularity as they can directly extract vectorized building footprints. This study benchmarks three line-segment detection (LSD) networks - L-CNN, ULSD, and F-Clip - against a strong semantic segmentation network - DeepLabV3+ - for building footprint extraction from very high resolution orthophotos across multiple regions with varied built-up morphology. Our evaluation on the considered urban areas revealed that LSD approaches generally deliver cleaner boundaries and more reliable roof topology than segmentation methods, whose high pixel scores mask boundary breaks. These findings indicate that when polygonal fidelity and downstream GIS usability are priorities, LSD pipelines could be superior for vectorized building footprint extraction compared to segmentation methods.