# DublinCity: Annotated LiDAR Point Cloud and its Applications

arXiv: https://arxiv.org/abs/1909.03613 · HTML: https://mlsanity.com/paper/1909.03613/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2019.

## Built on by (most cited first, 30 of 31)

- [KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D](https://mlsanity.com/paper/2109.13410/) (2021)
- [STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset](https://mlsanity.com/paper/2203.09065/) (2022)
- [SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds](https://mlsanity.com/paper/2201.04494/) (2022)
- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [The Hessigheim 3D (H3D) Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Textured Meshes from UAV LiDAR and Multi-View-Stereo](https://mlsanity.com/paper/2102.05346/) (2021)
- [UrbanBIS: a Large-scale Benchmark for Fine-grained Urban Building Instance Segmentation](https://mlsanity.com/paper/2305.02627/) (2023)
- [Building3D: An Urban-Scale Dataset and Benchmarks for Learning Roof Structures from Point Clouds](https://mlsanity.com/paper/2307.11914/) (2023)
- [SUM: A Benchmark Dataset of Semantic Urban Meshes](https://mlsanity.com/paper/2103.00355/) (2021)
- [CityRefer: Geography-aware 3D Visual Grounding Dataset on City-scale Point Cloud Data](https://mlsanity.com/paper/2310.18773/) (2023)
- [Online Segmentation of LiDAR Sequences: Dataset and Algorithm](https://mlsanity.com/paper/2206.08194/) (2022)
- [FRACTAL: An Ultra-Large-Scale Aerial Lidar Dataset for 3D Semantic Segmentation of Diverse Landscapes](https://mlsanity.com/paper/2405.04634/) (2024)
- [ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Segmentation](https://mlsanity.com/paper/2404.10699/) (2024)
- [OpenGF: An Ultra-Large-Scale Ground Filtering Dataset Built Upon Open ALS Point Clouds Around the World](https://mlsanity.com/paper/2101.09641/) (2021)
- [Deep Learning on 3D Semantic Segmentation: A Detailed Review](https://mlsanity.com/paper/2411.02104/) (2024)
- [ArcPro: Architectural Programs for Structured 3D Abstraction of Sparse Points](https://mlsanity.com/paper/2503.02745/) (2025)
- [Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans](https://mlsanity.com/paper/2304.09704/) (2023)
- [Learning Digital Terrain Models from Point Clouds: ALS2DTM Dataset and Rasterization-based GAN](https://mlsanity.com/paper/2206.03778/) (2022)
- [LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectives](https://mlsanity.com/paper/2503.18384/) (2025)
- [DeepSim-Nets: Deep Similarity Networks for Stereo Image Matching](https://mlsanity.com/paper/2304.08056/) (2023)
- [AerialMetric: Benchmarking and Adapting UAV Monocular Metric Depth Estimation in the Real World](https://mlsanity.com/paper/2606.29716/) (2026)
- [HRHD-HK: A benchmark dataset of high-rise and high-density urban scenes for 3D semantic segmentation of photogrammetric point clouds](https://mlsanity.com/paper/2307.07976/) (2023)
- [Quality Assessment of Super-Resolved Omnidirectional Image Quality Using Tangential Views](https://mlsanity.com/paper/2101.10396/) (2021)
- [Juggling With Representations: On the Information Transfer Between Imagery, Point Clouds, and Meshes for Multi-Modal Semantics](https://mlsanity.com/paper/2103.07348/) (2021)
- [SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshes](https://mlsanity.com/paper/2503.15300/) (2025)
- [SemanticBridge - A Dataset for 3D Semantic Segmentation of Bridges and Domain Gap Analysis](https://mlsanity.com/paper/2512.15369/) (2025)
- [Intensity Harmonization for Airborne LiDAR](https://mlsanity.com/paper/2105.01793/) (2021)
- [MODISSA: a multipurpose platform for the prototypical realization of vehicle-related applications using optical sensors](https://mlsanity.com/paper/2105.13580/) (2021)
- [Effective Utilisation of Multiple Open-Source Datasets to Improve Generalisation Performance of Point Cloud Segmentation Models](https://mlsanity.com/paper/2211.15877/) (2022)
- [Spatial Layout Consistency for 3D Semantic Segmentation](https://mlsanity.com/paper/2303.00939/) (2023)
- [WHU-Synthetic: A Synthetic Perception Dataset for 3-D Multitask Model Research](https://mlsanity.com/paper/2402.19059/) (2024)

## Abstract (arXiv)

Scene understanding of full-scale 3D models of an urban area remains a challenging task. While advanced computer vision techniques offer cost-effective approaches to analyse 3D urban elements, a precise and densely labelled dataset is quintessential. The paper presents the first-ever labelled dataset for a highly dense Aerial Laser Scanning (ALS) point cloud at city-scale. This work introduces a novel benchmark dataset that includes a manually annotated point cloud for over 260 million laser scanning points into 100'000 (approx.) assets from Dublin LiDAR point cloud [12] in 2015. Objects are labelled into 13 classes using hierarchical levels of detail from large (i.e., building, vegetation and ground) to refined (i.e., window, door and tree) elements. To validate the performance of our dataset, two different applications are showcased. Firstly, the labelled point cloud is employed for training Convolutional Neural Networks (CNNs) to classify urban elements. The dataset is tested on the well-known state-of-the-art CNNs (i.e., PointNet, PointNet++ and So-Net). Secondly, the complete ALS dataset is applied as detailed ground truth for city-scale image-based 3D reconstruction.