| --- |
| license: mit |
| language: |
| - en |
| pretty_name: DALES 2 |
| size_categories: |
| - 1G<n<10G |
| tags: |
| - lidar |
| - point-cloud |
| - aerial |
| - 3d-scene-understanding |
| - instance-segmentation |
| - semantic-segmentation |
| - scene-graph |
| - remote-sensing |
| task_categories: |
| - other |
| --- |
| |
| # DALES 2 |
|
|
| ## Dataset summary |
|
|
| **DALES 2** is a renovated large-scale **aerial LiDAR** benchmark for **3D scene understanding**, built on the geographic coverage of the original [DALES](https://udayton.edu/blogs/artssciences/2020-stories/dales-large-scale-aerial-lidar.php) release with **re-annotated** semantic and **instance** labels, refined taxonomy, and companion **scene-graph** structure (objects and relations) described in the USM3D workshop paper at CVPR 2026. |
|
|
| This Hub repository distributes the **annotated point clouds** (typically LAZ/LAS per tile). **Graph JSON** and the interactive **[graph editing tool](https://github.com/mbendjilali/dales-2)** live in the companion code repository (see [Scene graph tool](#scene-graph-tool)). |
|
|
| ### What is new vs. DALES Objects |
|
|
| - Human-in-the-loop revision of instances and semantics (e.g. powerline granularity per span, tree instances promoted from vegetation, fence as non-instantiable stuff). |
| - **15-class** semantic taxonomy with per-point **classification** and cross-class **unique instance IDs** within each tile (see [Class IDs](#class-ids)). |
| - Optional use with **relational graphs** (edges such as adjacent, near, support, extension) when generated with the official pipeline. |
|
|
| ## Supported tasks |
|
|
| - Semantic and instance segmentation on aerial LiDAR |
| - 3D detection / panoptic-style parsing in urban / infrastructure scenes |
| - Scene graph generation and reasoning (with external graph assets and tools) |
|
|
| ## Data modality |
|
|
| - **3D point clouds** (LAZ or LAS), aerial / large-footprint tiles |
|
|
| ## Class IDs |
|
|
| | ID | Class | Notes (high level) | |
| |---:|--------------|---------------------------| |
| | 0 | Ground | Stuff | |
| | 1 | Vegetation | Stuff | |
| | 2 | Car | Vehicle instance | |
| | 3 | Powerline | Conductor instance | |
| | 4 | Fence | Stuff | |
| | 5 | Tree | Tree instance | |
| | 6 | Pick-up | Vehicle instance | |
| | 7 | Van & Truck | Vehicle instance | |
| | 8 | Heavy-duty | Vehicle instance | |
| | 9 | Utility pole | Pole instance | |
| | 10 | Light pole | Pole instance | |
| | 11 | Traffic pole | Pole instance | |
| | 12 | Habitat | Building instance | |
| | 13 | Complex | Building instance | |
| | 14 | Annex | Building instance | |
|
|
| **Instance field:** Each point carries an **instance** attribute. **Non-stuff** objects use distinct positive instance IDs; **stuff** classes use instance **0** in the released convention. Instance IDs are **unique across semantic classes within a tile** (one ID does not appear on two different classes in the same file). |
|
|
| ## Dataset structure (repository layout) |
|
|
| Layout may evolve between releases; a typical layout is: |
|
|
| - One file per spatial **tile**, e.g. `*.laz` / `*.las` at the repository root or under a `tiles/` (or similar) prefix. |
| - Optional sidecar assets (metadata, splits, checksums) as you add them on the Hub. |
|
|
| After uploading, configure **Dataset > Files and versions** so the Hub (and `datasets`) can see supported patterns if you add a `load_dataset` script later. Until then, use **`huggingface_hub`** to download files (below). |
| |
| ## Download |
| |
| ### huggingface_hub (recommended for raw files) |
| |
| ```bash |
| pip install huggingface_hub |
| ``` |
| |
| ```python |
| from huggingface_hub import snapshot_download |
| |
| snapshot_download( |
| repo_id="mbendjilali/DALES-2", |
| repo_type="dataset", |
| local_dir="dales2", |
| ) |
| ``` |
| |
| ### Git LFS |
| |
| ```bash |
| git lfs install |
| git clone https://huggingface.co/datasets/mbendjilali/DALES-2 |
| ``` |
| |
| ## Scene graph tool |
| |
| Scene graphs (`graph_*.json`, `graph_*_edges.json`, `geom_*.json`) and the **web editor** are maintained in the **[Github](https://github.com/mbendjilali/dales-2)** codebase. Documentation, schema, and build instructions are in the project README shipped with that repository. |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite: |
|
|
| ```bibtex |
| @inproceedings{bendjilali2026dales2, |
| title = {{DALES} 2: A Renovated Aerial {LiDAR} Benchmark for 3D Scene Understanding}, |
| author = {Bendjilali, Moussa and Peyran, Claire and Velumani, Kaaviya and Mauri, Antoine and Luminari, Nicola and Alliez, Pierre}, |
| booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops}, |
| year = {2026}, |
| note = {USM3D workshop}, |
| } |
| ``` |
|
|
| (Update `pages` or volume fields when official workshop proceedings are available.) |
|
|
| ## License |
|
|
| This dataset is released under the **MIT License**, consistent with the Hub dataset metadata. |
|
|
| ## Acknowledgments |
|
|
| DALES 2 builds on the original **DALES** aerial LiDAR data and the **DALES Objects** instance benchmark. Please cite the original DALES and DALES Objects papers when comparing to prior work on that geography. |
|
|
| ## Contact |
|
|
| For questions about the release or benchmark, open a discussion on this dataset page or contact the authors via the affiliations listed in the paper. |
|
|