rsi/PixelsPointsPolygons
Object Detection
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Cannot get the config names for the dataset.
Error code: ConfigNamesError
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Message: Dataset scripts are no longer supported, but found PixelsPointsPolygons.py
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
config_names = get_dataset_config_names(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1031, in dataset_module_factory
raise e1 from None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 989, in dataset_module_factory
raise RuntimeError(f"Dataset scripts are no longer supported, but found {filename}")
RuntimeError: Dataset scripts are no longer supported, but found PixelsPointsPolygons.pyNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
The P3 Dataset: Pixels, Points and Polygons
for Multimodal Building Vectorization
Raphael Sulzer1,2 Liuyun Duan1 Nicolas Girard1 Florent Lafarge2
1LuxCarta Technology2Centre Inria d'Université Côte d'Azur
Figure 1: A view of our dataset of Zurich, Switzerland
Abstract
We present the P3 dataset, a large-scale multimodal benchmark for building vectorization, constructed from aerial LiDAR point clouds, high-resolution aerial imagery, and vectorized 2D building outlines, collected across three continents. The dataset contains over 10 billion LiDAR points with decimeter-level accuracy and RGB images at a ground sampling distance of 25 cm. While many existing datasets primarily focus on the image modality, P3 offers a complementary perspective by also incorporating dense 3D information. We demonstrate that LiDAR point clouds serve as a robust modality for predicting building polygons, both in hybrid and end-to-end learning frameworks. Moreover, fusing aerial LiDAR and imagery further improves accuracy and geometric quality of predicted polygons. The P3 dataset is publicly available, along with code and pretrained weights of three state-of-the-art models for building polygon prediction at https://github.com/raphaelsulzer/PixelsPointsPolygons.
Highlights
- A global, multimodal dataset of aerial images, aerial LiDAR point clouds and building outline polygons, available at huggingface.co/datasets/rsi/PixelsPointsPolygons
- A library for training and evaluating state-of-the-art deep learning methods on the dataset, available at github.com/raphaelsulzer/PixelsPointsPolygons
- Pretrained model weights, available at huggingface.co/rsi/PixelsPointsPolygons
- A paper with an extensive experimental validation, available at arxiv.org/abs/2505.15379
Dataset
Overview
Download
The recommended and fastest way to download the dataset is to run
pip install huggingface_hub
python scripts/download_dataset.py --dataset-root $DATA_ROOT
Optionally you can also download the dataset by running
git lfs install
git clone https://huggingface.co/datasets/rsi/PixelsPointsPolygons $DATA_ROOT
Both options will download the full dataset, including aerial images (as .tif), aerial lidar point clouds (as .copc.laz) and building polygon annotaions (as MS-COCO .json) into $DATA_ROOT . The size of the dataset is around 163GB.
Structure
📁 Click to expand dataset folder structure
PixelsPointsPolygons/data/224
├── annotations
│ ├── annotations_all_test.json
│ ├── annotations_all_train.json
│ └── annotations_all_val.json
│ ... (24 files total)
├── images
│ ├── train
│ │ ├── CH
│ │ │ ├── 0
│ │ │ │ ├── image0_CH_train.tif
│ │ │ │ ├── image1000_CH_train.tif
│ │ │ │ └── image1001_CH_train.tif
│ │ │ │ ... (5000 files total)
│ │ │ ├── 5000
│ │ │ │ ├── image5000_CH_train.tif
│ │ │ │ ├── image5001_CH_train.tif
│ │ │ │ └── image5002_CH_train.tif
│ │ │ │ ... (5000 files total)
│ │ │ └── 10000
│ │ │ ├── image10000_CH_train.tif
│ │ │ ├── image10001_CH_train.tif
│ │ │ └── image10002_CH_train.tif
│ │ │ ... (5000 files total)
│ │ │ ... (11 dirs total)
│ │ ├── NY
│ │ │ ├── 0
│ │ │ │ ├── image0_NY_train.tif
│ │ │ │ ├── image1000_NY_train.tif
│ │ │ │ └── image1001_NY_train.tif
│ │ │ │ ... (5000 files total)
│ │ │ ├── 5000
│ │ │ │ ├── image5000_NY_train.tif
│ │ │ │ ├── image5001_NY_train.tif
│ │ │ │ └── image5002_NY_train.tif
│ │ │ │ ... (5000 files total)
│ │ │ └── 10000
│ │ │ ├── image10000_NY_train.tif
│ │ │ ├── image10001_NY_train.tif
│ │ │ └── image10002_NY_train.tif
│ │ │ ... (5000 files total)
│ │ │ ... (11 dirs total)
│ │ └── NZ
│ │ ├── 0
│ │ │ ├── image0_NZ_train.tif
│ │ │ ├── image1000_NZ_train.tif
│ │ │ └── image1001_NZ_train.tif
│ │ │ ... (5000 files total)
│ │ ├── 5000
│ │ │ ├── image5000_NZ_train.tif
│ │ │ ├── image5001_NZ_train.tif
│ │ │ └── image5002_NZ_train.tif
│ │ │ ... (5000 files total)
│ │ └── 10000
│ │ ├── image10000_NZ_train.tif
│ │ ├── image10001_NZ_train.tif
│ │ └── image10002_NZ_train.tif
│ │ ... (5000 files total)
│ │ ... (11 dirs total)
│ ├── val
│ │ ├── CH
│ │ │ └── 0
│ │ │ ├── image0_CH_val.tif
│ │ │ ├── image100_CH_val.tif
│ │ │ └── image101_CH_val.tif
│ │ │ ... (529 files total)
│ │ ├── NY
│ │ │ └── 0
│ │ │ ├── image0_NY_val.tif
│ │ │ ├── image100_NY_val.tif
│ │ │ └── image101_NY_val.tif
│ │ │ ... (529 files total)
│ │ └── NZ
│ │ └── 0
│ │ ├── image0_NZ_val.tif
│ │ ├── image100_NZ_val.tif
│ │ └── image101_NZ_val.tif
│ │ ... (529 files total)
│ └── test
│ ├── CH
│ │ ├── 0
│ │ │ ├── image0_CH_test.tif
│ │ │ ├── image1000_CH_test.tif
│ │ │ └── image1001_CH_test.tif
│ │ │ ... (5000 files total)
│ │ ├── 5000
│ │ │ ├── image5000_CH_test.tif
│ │ │ ├── image5001_CH_test.tif
│ │ │ └── image5002_CH_test.tif
│ │ │ ... (5000 files total)
│ │ └── 10000
│ │ ├── image10000_CH_test.tif
│ │ ├── image10001_CH_test.tif
│ │ └── image10002_CH_test.tif
│ │ ... (4400 files total)
│ ├── NY
│ │ ├── 0
│ │ │ ├── image0_NY_test.tif
│ │ │ ├── image1000_NY_test.tif
│ │ │ └── image1001_NY_test.tif
│ │ │ ... (5000 files total)
│ │ ├── 5000
│ │ │ ├── image5000_NY_test.tif
│ │ │ ├── image5001_NY_test.tif
│ │ │ └── image5002_NY_test.tif
│ │ │ ... (5000 files total)
│ │ └── 10000
│ │ ├── image10000_NY_test.tif
│ │ ├── image10001_NY_test.tif
│ │ └── image10002_NY_test.tif
│ │ ... (4400 files total)
│ └── NZ
│ ├── 0
│ │ ├── image0_NZ_test.tif
│ │ ├── image1000_NZ_test.tif
│ │ └── image1001_NZ_test.tif
│ │ ... (5000 files total)
│ ├── 5000
│ │ ├── image5000_NZ_test.tif
│ │ ├── image5001_NZ_test.tif
│ │ └── image5002_NZ_test.tif
│ │ ... (5000 files total)
│ └── 10000
│ ├── image10000_NZ_test.tif
│ ├── image10001_NZ_test.tif
│ └── image10002_NZ_test.tif
│ ... (4400 files total)
├── lidar
│ ├── train
│ │ ├── CH
│ │ │ ├── 0
│ │ │ │ ├── lidar0_CH_train.copc.laz
│ │ │ │ ├── lidar1000_CH_train.copc.laz
│ │ │ │ └── lidar1001_CH_train.copc.laz
│ │ │ │ ... (5000 files total)
│ │ │ ├── 5000
│ │ │ │ ├── lidar5000_CH_train.copc.laz
│ │ │ │ ├── lidar5001_CH_train.copc.laz
│ │ │ │ └── lidar5002_CH_train.copc.laz
│ │ │ │ ... (5000 files total)
│ │ │ └── 10000
│ │ │ ├── lidar10000_CH_train.copc.laz
│ │ │ ├── lidar10001_CH_train.copc.laz
│ │ │ └── lidar10002_CH_train.copc.laz
│ │ │ ... (5000 files total)
│ │ │ ... (11 dirs total)
│ │ ├── NY
│ │ │ ├── 0
│ │ │ │ ├── lidar0_NY_train.copc.laz
│ │ │ │ ├── lidar10_NY_train.copc.laz
│ │ │ │ └── lidar1150_NY_train.copc.laz
│ │ │ │ ... (1071 files total)
│ │ │ ├── 5000
│ │ │ │ ├── lidar5060_NY_train.copc.laz
│ │ │ │ ├── lidar5061_NY_train.copc.laz
│ │ │ │ └── lidar5062_NY_train.copc.laz
│ │ │ │ ... (2235 files total)
│ │ │ └── 10000
│ │ │ ├── lidar10000_NY_train.copc.laz
│ │ │ ├── lidar10001_NY_train.copc.laz
│ │ │ └── lidar10002_NY_train.copc.laz
│ │ │ ... (4552 files total)
│ │ │ ... (11 dirs total)
│ │ └── NZ
│ │ ├── 0
│ │ │ ├── lidar0_NZ_train.copc.laz
│ │ │ ├── lidar1000_NZ_train.copc.laz
│ │ │ └── lidar1001_NZ_train.copc.laz
│ │ │ ... (5000 files total)
│ │ ├── 5000
│ │ │ ├── lidar5000_NZ_train.copc.laz
│ │ │ ├── lidar5001_NZ_train.copc.laz
│ │ │ └── lidar5002_NZ_train.copc.laz
│ │ │ ... (5000 files total)
│ │ └── 10000
│ │ ├── lidar10000_NZ_train.copc.laz
│ │ ├── lidar10001_NZ_train.copc.laz
│ │ └── lidar10002_NZ_train.copc.laz
│ │ ... (4999 files total)
│ │ ... (11 dirs total)
│ ├── val
│ │ ├── CH
│ │ │ └── 0
│ │ │ ├── lidar0_CH_val.copc.laz
│ │ │ ├── lidar100_CH_val.copc.laz
│ │ │ └── lidar101_CH_val.copc.laz
│ │ │ ... (529 files total)
│ │ ├── NY
│ │ │ └── 0
│ │ │ ├── lidar0_NY_val.copc.laz
│ │ │ ├── lidar100_NY_val.copc.laz
│ │ │ └── lidar101_NY_val.copc.laz
│ │ │ ... (529 files total)
│ │ └── NZ
│ │ └── 0
│ │ ├── lidar0_NZ_val.copc.laz
│ │ ├── lidar100_NZ_val.copc.laz
│ │ └── lidar101_NZ_val.copc.laz
│ │ ... (529 files total)
│ └── test
│ ├── CH
│ │ ├── 0
│ │ │ ├── lidar0_CH_test.copc.laz
│ │ │ ├── lidar1000_CH_test.copc.laz
│ │ │ └── lidar1001_CH_test.copc.laz
│ │ │ ... (5000 files total)
│ │ ├── 5000
│ │ │ ├── lidar5000_CH_test.copc.laz
│ │ │ ├── lidar5001_CH_test.copc.laz
│ │ │ └── lidar5002_CH_test.copc.laz
│ │ │ ... (5000 files total)
│ │ └── 10000
│ │ ├── lidar10000_CH_test.copc.laz
│ │ ├── lidar10001_CH_test.copc.laz
│ │ └── lidar10002_CH_test.copc.laz
│ │ ... (4400 files total)
│ ├── NY
│ │ ├── 0
│ │ │ ├── lidar0_NY_test.copc.laz
│ │ │ ├── lidar1000_NY_test.copc.laz
│ │ │ └── lidar1001_NY_test.copc.laz
│ │ │ ... (4964 files total)
│ │ ├── 5000
│ │ │ ├── lidar5000_NY_test.copc.laz
│ │ │ ├── lidar5001_NY_test.copc.laz
│ │ │ └── lidar5002_NY_test.copc.laz
│ │ │ ... (4953 files total)
│ │ └── 10000
│ │ ├── lidar10000_NY_test.copc.laz
│ │ ├── lidar10001_NY_test.copc.laz
│ │ └── lidar10002_NY_test.copc.laz
│ │ ... (4396 files total)
│ └── NZ
│ ├── 0
│ │ ├── lidar0_NZ_test.copc.laz
│ │ ├── lidar1000_NZ_test.copc.laz
│ │ └── lidar1001_NZ_test.copc.laz
│ │ ... (5000 files total)
│ ├── 5000
│ │ ├── lidar5000_NZ_test.copc.laz
│ │ ├── lidar5001_NZ_test.copc.laz
│ │ └── lidar5002_NZ_test.copc.laz
│ │ ... (5000 files total)
│ └── 10000
│ ├── lidar10000_NZ_test.copc.laz
│ ├── lidar10001_NZ_test.copc.laz
│ └── lidar10002_NZ_test.copc.laz
│ ... (4400 files total)
└── ffl
├── train
│ ├── CH
│ │ ├── 0
│ │ │ ├── image0_CH_train.pt
│ │ │ ├── image1000_CH_train.pt
│ │ │ └── image1001_CH_train.pt
│ │ │ ... (5000 files total)
│ │ ├── 5000
│ │ │ ├── image5000_CH_train.pt
│ │ │ ├── image5001_CH_train.pt
│ │ │ └── image5002_CH_train.pt
│ │ │ ... (5000 files total)
│ │ └── 10000
│ │ ├── image10000_CH_train.pt
│ │ ├── image10001_CH_train.pt
│ │ └── image10002_CH_train.pt
│ │ ... (5000 files total)
│ │ ... (11 dirs total)
│ ├── NY
│ │ ├── 0
│ │ │ ├── image0_NY_train.pt
│ │ │ ├── image1000_NY_train.pt
│ │ │ └── image1001_NY_train.pt
│ │ │ ... (5000 files total)
│ │ ├── 5000
│ │ │ ├── image5000_NY_train.pt
│ │ │ ├── image5001_NY_train.pt
│ │ │ └── image5002_NY_train.pt
│ │ │ ... (5000 files total)
│ │ └── 10000
│ │ ├── image10000_NY_train.pt
│ │ ├── image10001_NY_train.pt
│ │ └── image10002_NY_train.pt
│ │ ... (5000 files total)
│ │ ... (11 dirs total)
│ ├── NZ
│ │ ├── 0
│ │ │ ├── image0_NZ_train.pt
│ │ │ ├── image1000_NZ_train.pt
│ │ │ └── image1001_NZ_train.pt
│ │ │ ... (5000 files total)
│ │ ├── 5000
│ │ │ ├── image5000_NZ_train.pt
│ │ │ ├── image5001_NZ_train.pt
│ │ │ └── image5002_NZ_train.pt
│ │ │ ... (5000 files total)
│ │ └── 10000
│ │ ├── image10000_NZ_train.pt
│ │ ├── image10001_NZ_train.pt
│ │ └── image10002_NZ_train.pt
│ │ ... (5000 files total)
│ │ ... (11 dirs total)
│ ├── processed-flag-all
│ ├── processed-flag-CH
│ └── processed-flag-NY
│ ... (8 files total)
├── val
│ ├── CH
│ │ └── 0
│ │ ├── image0_CH_val.pt
│ │ ├── image100_CH_val.pt
│ │ └── image101_CH_val.pt
│ │ ... (529 files total)
│ ├── NY
│ │ └── 0
│ │ ├── image0_NY_val.pt
│ │ ├── image100_NY_val.pt
│ │ └── image101_NY_val.pt
│ │ ... (529 files total)
│ ├── NZ
│ │ └── 0
│ │ ├── image0_NZ_val.pt
│ │ ├── image100_NZ_val.pt
│ │ └── image101_NZ_val.pt
│ │ ... (529 files total)
│ ├── processed-flag-all
│ ├── processed-flag-CH
│ └── processed-flag-NY
│ ... (8 files total)
└── test
├── CH
│ ├── 0
│ │ ├── image0_CH_test.pt
│ │ ├── image1000_CH_test.pt
│ │ └── image1001_CH_test.pt
│ │ ... (5000 files total)
│ ├── 5000
│ │ ├── image5000_CH_test.pt
│ │ ├── image5001_CH_test.pt
│ │ └── image5002_CH_test.pt
│ │ ... (5000 files total)
│ └── 10000
│ ├── image10000_CH_test.pt
│ ├── image10001_CH_test.pt
│ └── image10002_CH_test.pt
│ ... (4400 files total)
├── NY
│ ├── 0
│ │ ├── image0_NY_test.pt
│ │ ├── image1000_NY_test.pt
│ │ └── image1001_NY_test.pt
│ │ ... (5000 files total)
│ ├── 5000
│ │ ├── image5000_NY_test.pt
│ │ ├── image5001_NY_test.pt
│ │ └── image5002_NY_test.pt
│ │ ... (5000 files total)
│ └── 10000
│ ├── image10000_NY_test.pt
│ ├── image10001_NY_test.pt
│ └── image10002_NY_test.pt
│ ... (4400 files total)
├── NZ
│ ├── 0
│ │ ├── image0_NZ_test.pt
│ │ ├── image1000_NZ_test.pt
│ │ └── image1001_NZ_test.pt
│ │ ... (5000 files total)
│ ├── 5000
│ │ ├── image5000_NZ_test.pt
│ │ ├── image5001_NZ_test.pt
│ │ └── image5002_NZ_test.pt
│ │ ... (5000 files total)
│ └── 10000
│ ├── image10000_NZ_test.pt
│ ├── image10001_NZ_test.pt
│ └── image10002_NZ_test.pt
│ ... (4400 files total)
├── processed-flag-all
├── processed-flag-CH
└── processed-flag-NY
... (8 files total)
Pretrained model weights
Download
The recommended and fastest way to download the pretrained model weights is to run
python scripts/download_pretrained.py --model-root $MODEL_ROOT
Optionally you can also download the weights by running
git clone https://huggingface.co/rsi/PixelsPointsPolygons $MODEL_ROOT
Both options will download all checkpoints (as .pth) and results presented in the paper (as MS-COCO .json) into $MODEL_ROOT .
Code
Download
git clone https://github.com/raphaelsulzer/PixelsPointsPolygons
Installation
To create a conda environment named p3 and install the repository as a python package with all dependencies run
bash install.sh
or, if you want to manage the environment yourself run
pip install -r requirements-torch-cuda.txt
pip install .
⚠️ Warning: The implementation of the LiDAR point cloud encoder uses Open3D-ML. Currently, Open3D-ML officially only supports the PyTorch version specified in requirements-torch-cuda.txt.
Setup
The project supports hydra configuration which allows to modify any parameter either from a .yaml file or directly from the command line.
To setup the project structure we recommend to specify your $DATA_ROOT and $MODEL_ROOT in config/host/default.yaml.
To view all available configuration options run
python scripts/train.py --help
Predict demo tile
After downloading the model weights and setting up the code you can predict a demo tile by running
python scripts/predict_demo.py checkpoint=best_val_iou experiment=$MODEL_$MODALITY +image_file=demo_data/image0_CH_val.tif +lidar_file=demo_data/lidar0_CH_val.copc.laz
At least one of image_file or lidar_file has to be specified. $MODEL can be one of the following: ffl, hisup or p2p. $MODALITY can be image, lidar or fusion.
The result will be stored in prediction.png.
Reproduce paper results
To reproduce the results from the paper you can run the following commands
python scripts/modality_ablation.py
python scripts/lidar_density_ablation.py
python scripts/all_countries.py
Custom training, prediction and evaluation
We recommend to first setup a custom experiment file $EXP_FILE in config/experiment/ following the structure of one of the existing files, e.g. ffl_fusion.yaml. You can then run
# train your model (on multiple GPUs)
torchrun --nproc_per_node=$NUM_GPU scripts/train.py experiment=$EXP_FILE
# predict the test set with your model (on multiple GPUs)
torchrun --nproc_per_node=$NUM_GPU scripts/predict.py experiment=$EXP_FILE evaluation=test checkpoint=best_val_iou
# evaluate your prediction of the test set
python scripts/evaluate.py experiment=$EXP_FILE evaluation=test checkpoint=best_val_iou
You could also continue training from a provided pretrained model with
# train your model (on a single GPU)
python scripts/train.py experiment=p2p_fusion checkpoint=latest
Citation
If you use our work please cite
@misc{sulzer2025p3datasetpixelspoints,
title={The P$^3$ dataset: Pixels, Points and Polygons for Multimodal Building Vectorization},
author={Raphael Sulzer and Liuyun Duan and Nicolas Girard and Florent Lafarge},
year={2025},
eprint={2505.15379},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2505.15379},
}
Acknowledgements
This repository benefits from the following open-source work. We thank the authors for their great work.
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