# DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentation

arXiv: https://arxiv.org/abs/2004.11985 · HTML: https://mlsanity.com/paper/2004.11985/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2020.

## Built on by (most cited first, 30 of 66)

- [OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation](https://mlsanity.com/paper/2309.00616/) (2023)
- [SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds](https://mlsanity.com/paper/2104.04891/) (2021)
- [Efficient 3D Semantic Segmentation with Superpoint Transformer](https://mlsanity.com/paper/2306.08045/) (2023)
- [STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset](https://mlsanity.com/paper/2203.09065/) (2022)
- [Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling](https://mlsanity.com/paper/2107.02389/) (2021)
- [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)
- [GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds](https://mlsanity.com/paper/2305.16404/) (2023)
- [Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping](https://mlsanity.com/paper/2111.11348/) (2021)
- [UrbanBIS: a Large-scale Benchmark for Fine-grained Urban Building Instance Segmentation](https://mlsanity.com/paper/2305.02627/) (2023)
- [SUM: A Benchmark Dataset of Semantic Urban Meshes](https://mlsanity.com/paper/2103.00355/) (2021)
- [City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Clouds](https://mlsanity.com/paper/2201.10276/) (2022)
- [CityRefer: Geography-aware 3D Visual Grounding Dataset on City-scale Point Cloud Data](https://mlsanity.com/paper/2310.18773/) (2023)
- [LitePT: Lighter Yet Stronger Point Transformer](https://mlsanity.com/paper/2512.13689/) (2025)
- [Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering](https://mlsanity.com/paper/2401.06704/) (2024)
- [An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models](https://mlsanity.com/paper/2405.14870/) (2024)
- [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)
- [TUM-FAcCADE: Reviewing and enriching point cloud benchmarks for faccade segmentation](https://mlsanity.com/paper/2304.07140/) (2023)
- [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)
- [Human Vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scene](https://mlsanity.com/paper/2301.12972/) (2023)
- [LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectives](https://mlsanity.com/paper/2503.18384/) (2025)
- [COVERED, CollabOratiVE Robot Environment Dataset for 3D Semantic segmentation](https://mlsanity.com/paper/2302.12656/) (2023)
- [Advancements in Point Cloud-Based 3D Defect Detection and Classification for Industrial Systems: A Comprehensive Survey](https://mlsanity.com/paper/2402.12923/) (2024)
- [3DSES: an indoor Lidar point cloud segmentation dataset with real and pseudo-labels from a 3D model](https://mlsanity.com/paper/2501.17534/) (2025)
- [3DAeroRelief: The first 3D Benchmark UAV Dataset for Post-Disaster Assessment](https://mlsanity.com/paper/2509.11097/) (2025)
- [sat2pc: Estimating Point Cloud of Building Roofs from 2D Satellite Images](https://mlsanity.com/paper/2205.12464/) (2022)

## Abstract (arXiv)

We present the Dayton Annotated LiDAR Earth Scan (DALES) data set, a new large-scale aerial LiDAR data set with over a half-billion hand-labeled points spanning 10 square kilometers of area and eight object categories. Large annotated point cloud data sets have become the standard for evaluating deep learning methods. However, most of the existing data sets focus on data collected from a mobile or terrestrial scanner with few focusing on aerial data. Point cloud data collected from an Aerial Laser Scanner (ALS) presents a new set of challenges and applications in areas such as 3D urban modeling and large-scale surveillance. DALES is the most extensive publicly available ALS data set with over 400 times the number of points and six times the resolution of other currently available annotated aerial point cloud data sets. This data set gives a critical number of expert verified hand-labeled points for the evaluation of new 3D deep learning algorithms, helping to expand the focus of current algorithms to aerial data. We describe the nature of our data, annotation workflow, and provide a benchmark of current state-of-the-art algorithm performance on the DALES data set.