# Automated 3D reconstruction of LoD2 and LoD1 models for all 10 million buildings of the Netherlands

arXiv: https://arxiv.org/abs/2201.01191 · HTML: https://mlsanity.com/paper/2201.01191/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 23 references that are in the map.
- First posted on arXiv 30 Dec 2021.

## Builds on (most cited first, 1 of 1)

- [CityJSON: a compact and easy-to-use encoding of the CityGML data model](https://mlsanity.com/paper/1902.09155/) (2019)

## Built on by (most cited first, 14 of 14)

- [GlobalBuildingAtlas: An Open Global and Complete Dataset of Building Polygons, Heights and LoD1 3D Models](https://mlsanity.com/paper/2506.04106/) (2025)
- [LoD-Loc: Aerial Visual Localization using LoD 3D Map with Neural Wireframe Alignment](https://mlsanity.com/paper/2410.12269/) (2024)
- [PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Clouds](https://mlsanity.com/paper/2307.08636/) (2023)
- [BuilDiff: 3D Building Shape Generation using Single-Image Conditional Point Cloud Diffusion Models](https://mlsanity.com/paper/2309.00158/) (2023)
- [LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment](https://mlsanity.com/paper/2507.00659/) (2025)
- [PG-SAG: Parallel Gaussian Splatting for Fine-Grained Large-Scale Urban Buildings Reconstruction via Semantic-Aware Grouping](https://mlsanity.com/paper/2501.01677/) (2025)
- [OpenCity3D: What do Vision-Language Models know about Urban Environments?](https://mlsanity.com/paper/2503.16776/) (2025)
- [MeSS: City Mesh-Guided Outdoor Scene Generation with Cross-View Consistent Diffusion](https://mlsanity.com/paper/2508.15169/) (2025)
- [Building Floor Number Estimation from Crowdsourced Street-Level Images: Munich Dataset and Baseline Method](https://mlsanity.com/paper/2505.18021/) (2025)
- [3dSAGER: Geospatial Entity Resolution over 3D Objects (Technical Report)](https://mlsanity.com/paper/2511.06300/) (2025)
- [Towards AI-Architecture Liberty: A Comprehensive Survey on Design and Generation of Virtual Architecture by Deep Learning](https://mlsanity.com/paper/2305.00510/) (2023)
- [Unsupervised Roofline Extraction from True Orthophotos for LoD2 Building Model Reconstruction](https://mlsanity.com/paper/2310.01067/) (2023)
- [ArchEGraph: A Large-Scale Graph Dataset for Geometry-Topology-Physics Aligned Building Energy Modeling](https://mlsanity.com/paper/2608.06772/) (2026)
- [ProxyBuild: Text-Guided Structured 3D Building Generation with Mesh-Anchored Procedural Proxies](https://mlsanity.com/paper/2609.23386/) (2026)

## Abstract (arXiv)

In this paper we present our workflow to automatically reconstruct 3D building models based on 2D building polygons and a LiDAR point cloud. The workflow generates models at different levels of detail (LoDs) to support data requirements of different applications from one consistent source. Specific attention has been paid to make the workflow robust to quickly run a new iteration in case of improvements in an algorithm or in case new input data become available. The quality of the reconstructed data highly depends on the quality of the input data and is monitored in several steps of the process. A 3D viewer has been developed to view and download the openly available 3D data at different LoDs in different formats. The workflow has been applied to all 10 million buildings of The Netherlands. The 3D service will be updated after new input data becomes available.