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New P3 Dataset combines LiDAR, imagery for building vectorization

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced the P$^3$ Dataset, a large-scale multimodal benchmark designed for building vectorization. This dataset combines aerial LiDAR point clouds, high-resolution aerial imagery, and vectorized 2D building outlines collected globally. It features over 10 billion LiDAR points with decimeter-level accuracy and RGB images at a 25-centimeter ground sampling distance. The P$^3$ dataset aims to provide a more comprehensive perspective than existing image-focused datasets by including dense 3D information, demonstrating the effectiveness of LiDAR for predicting building polygons and showing that fusing LiDAR and imagery further enhances accuracy and geometric quality. AI

IMPACT Provides a new benchmark for multimodal AI models in geospatial analysis and urban planning.

RANK_REASON The cluster describes a new dataset and associated research paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New P3 Dataset combines LiDAR, imagery for building vectorization

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The cluster describes a new dataset and associated research paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Raphael Sulzer, Liuyun Duan, Nicolas Girard, Florent Lafarge ·

    The P$^3$ Dataset: Pixels, Points and Polygons for Multimodal Building Vectorization

    arXiv:2505.15379v2 Announce Type: replace Abstract: We present the P$^3$ 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…

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