We present the P dataset, a large-scale multimodal benchmark for building vectorization, constructed from aerial LiDAR point clouds, high-resolution aerial imagery, and vectorized 2D building outlines, collected across three continents. The dataset contains over 10 billion LiDAR points with decimeter-level accuracy and RGB images at a ground sampling distance of 25 centimeter. While many existing datasets primarily focus on the image modality, P offers a complementary perspective by also incorporating dense 3D information. We demonstrate that LiDAR point clouds serve as a robust modality for predicting building polygons, both in hybrid and end-to-end learning frameworks. Moreover, fusing aerial LiDAR and imagery further improves accuracy and geometric quality of predicted polygons. The P dataset is publicly available, along with code and pretrained weights of three state-of-the-art models for building polygon prediction atthis https URL.
View on arXiv@article{sulzer2025_2505.15379,
title={ The P$^3$ dataset: Pixels, Points and Polygons for Multimodal Building Vectorization },
author={ Raphael Sulzer and Liuyun Duan and Nicolas Girard and Florent Lafarge },
journal={arXiv preprint arXiv:2505.15379},
year={ 2025 }
}