Accurate building roof modeling is fundamental to urban analytics, digital twins, and 3D city reconstruction. However, progress in deep learning–based reconstruction is constrained by the limited availability of diverse, high-resolution datasets with detailed geometric annotations. This study introduces ROOFVIP dataset, a large-scale benchmark featuring very high-resolution RGB orthophotos paired with 2D roof vectors that capture diverse urban morphologies across Munich, Germany. Following Level of Detail (LoD) 2 principles, ROOFVIP encompasses a broad range of roof geometries and architectural complexities, providing a robust foundation for evaluating both segmentation- and vectorization-based reconstruction methods. Two reconstruction paradigms are examined: a two-step segmentation-based approach (Cascade Mask R-CNN, Mask R-CNN, SOLOV2, YOLACT) and a one-step direct vector prediction approach (HEAT, PolyRoof). ImageNet-pretrained region-based models, particularly Mask R-CNN and Cascade Mask R-CNN, achieve the highest segmentation accuracy, effectively delineating complex roof boundaries while revealing challenges in small or irregular structures. Geometry-based models exhibit complementary strengths: HEAT prioritizes topological regularity, while PolyRoof emphasizes geometric precision. Although performance metrics are lower than those on simpler datasets such as HEAT and Roof Intuitive, ROOFVIP effectively exposes the challenges of geometric diversity and scale variation, serving as a rigorous benchmark for future research. The dataset includes predefined training, validation, and test splits, enabling consistent benchmarking across methods. By providing a challenging and diverse geometric landscape, ROOFVIP aims to advance geometry-aware deep learning approaches and support scalable, high-fidelity 3D urban modeling. The dataset is publicly available through the project page at <code>https://chaikalamrullah.github.io/RoofVIP/</code>.