# FRACTAL: An Ultra-Large-Scale Aerial Lidar Dataset for 3D Semantic Segmentation of Diverse Landscapes

arXiv: https://arxiv.org/abs/2405.04634 · HTML: https://mlsanity.com/paper/2405.04634/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 7 May 2024.

## Builds on (most cited first, 12 of 12)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://mlsanity.com/paper/1911.11236/) (2019)
- [Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark](https://mlsanity.com/paper/1704.03847/) (2017)
- [DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentation](https://mlsanity.com/paper/2004.11985/) (2020)
- [FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery](https://mlsanity.com/paper/2310.13336/) (2023)
- [LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery](https://mlsanity.com/paper/2005.02264/) (2020)
- [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)
- [DublinCity: Annotated LiDAR Point Cloud and its Applications](https://mlsanity.com/paper/1909.03613/) (2019)
- [PureForest: A Large-Scale Aerial Lidar and Aerial Imagery Dataset for Tree Species Classification in Monospecific Forests](https://mlsanity.com/paper/2404.12064/) (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)

## Built on by (most cited first, 11 of 11)

- [Revisiting Embodied Chain-of-Thought for Generalizable Robot Manipulation](https://mlsanity.com/paper/2606.03784/) (2026)
- [LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectives](https://mlsanity.com/paper/2503.18384/) (2025)
- [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)
- [Turin3D: Evaluating Adaptation Strategies under Label Scarcity in Urban LiDAR Segmentation with Semi-Supervised Techniques](https://mlsanity.com/paper/2504.05882/) (2025)
- [3D Semantic Segmentation for Post-Disaster Assessment](https://mlsanity.com/paper/2512.24593/) (2025)
- [Ghost-FWL: A Large-Scale Full-Waveform LiDAR Dataset for Ghost Detection and Removal](https://mlsanity.com/paper/2603.28224/) (2026)
- [Benchmarking Deep Learning Models for Aerial LiDAR Point Cloud Semantic Segmentation under Real Acquisition Conditions: A Case Study in Navarre](https://mlsanity.com/paper/2603.22229/) (2026)
- [Spatially-Aware Evaluation Framework for Aerial LiDAR Point Cloud Semantic Segmentation: Distance-Based Metrics on Challenging Regions](https://mlsanity.com/paper/2603.22420/) (2026)
- [How Sampling Strategy Affects Imbalance Mitigation in LiDAR Segmentation: A Study of Structured vs. Random Point-Based Architectures](https://mlsanity.com/paper/2608.16673/) (2026)
- [Atomizer-IO: Beyond Pixels, Patches and Grids](https://mlsanity.com/paper/2609.40320/) (2026)

## Abstract (arXiv)

Mapping agencies are increasingly adopting Aerial Lidar Scanning (ALS) as a new tool to map buildings and other above-ground structures. Processing ALS data at scale requires efficient point classification methods that perform well over highly diverse territories. Large annotated Lidar datasets are needed to evaluate these classification methods, however, current Lidar benchmarks have restricted scope and often cover a single urban area. To bridge this data gap, we introduce the FRench ALS Clouds from TArgeted Landscapes (FRACTAL) dataset: an ultra-large-scale aerial Lidar dataset made of 100,000 dense point clouds with high quality labels for 7 semantic classes and spanning 250 km2. FRACTAL achieves high spatial and semantic diversity by explicitly sampling rare classes and challenging landscapes from five different regions of France. We describe the data collection, annotation, and curation process of the dataset. We provide baseline semantic segmentation results using a state of the art 3D point cloud classification model. FRACTAL aims to support the development of 3D deep learning approaches for large-scale land monitoring.