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Jakarto datasets for 3d detection challenge of urban assets

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We built that python API to share some real-world 3d lidar datasets of urban assets. We hope to help some of you to develop and test algorithms about 3d lidar processing.

jakarto car detection

Those datasets have been gathered with the Jakarto truck. jakarto truck

Installation

This API requires python 3.6+.

pip install jakarto-datasets

Usage

from jakarto_datasets.datasets.storm_drains import StormDrainsDataset

# Load dataset
storm_drains_2019 = StormDrainsDataset()

for data in storm_drains_2019.training_set:
    coordinates = data.get_coordinates_data()
    lidar_data = data.get_lidar_data()

    label = data.get_label_lidar_data()

    print(data)
    print(coordinates.shape)
    print(lidar_data.shape)
    print(label.shape)

    print(lidar_data['intensity'])

Datasets

datasets year 3d lidar label raster mask len(training_set) len(testing_set) examples description
storm drains 2019 ✔️ ✔️ ✔️ ✔️ 223 150 see examples see details

Benchmarks

We will be more than happy to share your experiments.

datasets title authors links description
storm drains 2019 balanced random forest Jakarto team link Use a deadly simple balanced random forest to classify each point from lidar data. Although it doesn't use spatial information, it allowed Jakarto to detect ~25% of storm drains. Those storm drains will be added to the Jakarto storm drains 2020 dataset.
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Citation

If you find this work useful and wish to refer to, please consider the following BibTeX entry:

@MISC{jakarto_datasets,
    author = {Loic Messal and Cedric Pelletier and {Jakarto Cartographie 3d team}},
    title = {Jakarto datasets},
    year = {2019},
    howpublished={\url{https://github.com/jakarto3d/jakarto_datasets}}
}

A github star may also help.

Contact

If you want to email us, please send an email to contact@jakarto.com.

License

This project is licensed under the terms of the MIT license. (see LICENSE.txt file for details).

Metadata

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