# PCFootprint: A Large-Scale Dataset and Benchmark for Vectorized Building Footprint Extraction from Aerial LiDAR Point Clouds

arXiv: https://arxiv.org/abs/2606.20455 · HTML: https://mlsanity.com/paper/2606.20455/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 71 references that are in the map.
- First posted on arXiv 18 Jun 2026.

## Builds on (most cited first, 24 of 24)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [SAM 3: Segment Anything with Concepts](https://mlsanity.com/paper/2511.16719/) (2025)
- [SpaceNet: A Remote Sensing Dataset and Challenge Series](https://mlsanity.com/paper/1807.01232/) (2018)
- [Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways](https://mlsanity.com/paper/2003.08284/) (2020)
- [DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentation](https://mlsanity.com/paper/2004.11985/) (2020)
- [MAP-Net: Multi Attending Path Neural Network for Building Footprint Extraction from Remote Sensed Imagery](https://mlsanity.com/paper/1910.12060/) (2019)
- [V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detection](https://mlsanity.com/paper/2308.04409/) (2023)
- [City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Clouds](https://mlsanity.com/paper/2201.10276/) (2022)
- [VertexRegen: Mesh Generation with Continuous Level of Detail](https://mlsanity.com/paper/2508.09062/) (2025)
- [SegEarth-OV3: Exploring SAM 3 for Open-Vocabulary Semantic Segmentation in Remote Sensing Images](https://mlsanity.com/paper/2512.08730/) (2025)
- [A Unified Framework with Multimodal Fine-tuning for Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2410.11160/) (2024)
- [BuildMapper: A Fully Learnable Framework for Vectorized Building Contour Extraction](https://mlsanity.com/paper/2211.03373/) (2022)
- [A diverse large-scale building dataset and a novel plug-and-play domain generalization method for building extraction](https://mlsanity.com/paper/2208.10004/) (2022)
- [Pix2Poly: A Sequence Prediction Method for End-to-end Polygonal Building Footprint Extraction from Remote Sensing Imagery](https://mlsanity.com/paper/2412.07899/) (2024)
- [Dynamic Dictionary Learning for Remote Sensing Image Segmentation](https://mlsanity.com/paper/2503.06683/) (2025)
- [PolyR-CNN: R-CNN for end-to-end polygonal building outline extraction](https://mlsanity.com/paper/2407.14912/) (2024)
- [RoIPoly: Vectorized Building Outline Extraction Using Vertex and Logit Embeddings](https://mlsanity.com/paper/2407.14920/) (2024)
- [P2PFormer: A Primitive-to-polygon Method for Regular Building Contour Extraction from Remote Sensing Images](https://mlsanity.com/paper/2406.02930/) (2024)
- [HoliTracer: Holistic Vectorization of Geographic Objects from Large-Size Remote Sensing Imagery](https://mlsanity.com/paper/2507.16251/) (2025)
- [Tortho-Gaussian: Splatting True Digital Orthophoto Maps](https://mlsanity.com/paper/2411.19594/) (2024)
- [Terrain-Informed Self-Supervised Learning: Enhancing Building Footprint Extraction from LiDAR Data with Limited Annotations](https://mlsanity.com/paper/2311.01188/) (2023)
- [DPPD: Deformable Polar Polygon Object Detection](https://mlsanity.com/paper/2304.02250/) (2023)
- [High-Quality Spatial Reconstruction and Orthoimage Generation Using Efficient 2D Gaussian Splatting](https://mlsanity.com/paper/2503.19703/) (2025)

## Abstract (arXiv)

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 num33000 tiles derived from the Estonian Land and Spatial Development Board, covering diverse urban and rural landscapes. Each tile spans qtyproduct128 x 128m with systematically aligned vectorized footprints aligned to point clouds. The dataset includes a num3000 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 urlhttps://huggingface.co/datasets/Haoyuan-Shen/PCFootprint.