🤖 AI Summary
Optical imagery-based building footprint extraction is often compromised by occlusion, perspective distortion, and the absence of elevation data, leading to incomplete or misaligned results. This work proposes the first large-scale vectorized building footprint dataset and benchmark specifically designed for airborne LiDAR point clouds, encompassing 33,000 urban and rural tiles of size 128×128 meters, along with 3,000 cross-domain test tiles to evaluate geographic generalization. The dataset provides precisely aligned vector footprints coupled with elevation information, enabling fine-grained modeling and cross-regional studies. Through comprehensive baseline experiments, this study highlights key challenges including high intra-class variability, data imbalance, and noise, thereby advancing research in urban perception and building modeling.
📝 Abstract
Building footprint extraction is a fundamental task in photogrammetry, remote sensing, and computer vision. Recent image-based methods have achieved remarkable progress in extracting vectorized footprints from high-resolution optical imagery. However, optical imagery inherently susceptible to occlusions, perspective distortions, and residual relief displacement, yielding incomplete or misaligned footprint extraction. Furthermore, the lack of explicit elevation information limits its direct applicability to Level of Detail building modeling. In this paper, we present PCFootprint, the first large-scale public dataset for footprint extraction from airborne laser scanning point clouds. PCFootprint comprises \num{33000} tiles derived from the Estonian Land and Spatial Development Board, covering diverse urban and rural landscapes. Each tile spans \qtyproduct{128 x 128}{\m} with systematically aligned vectorized footprints aligned to point clouds. The dataset includes a \num{3000} tiles cross-domain test set for evaluating generalization across geographic regions. We establish comprehensive benchmarks by evaluating mainstream methods. Experimental results reveal significant challenges including high intra-class variance, data imbalance, and noise across complex geospatial environments. We believe PCFootprint will advance future research in building modeling, urban scene understanding, and geospatial analysis. The PCFootprint dataset is publicly available at \url{https://huggingface.co/datasets/Haoyuan-Shen/PCFootprint}.