Jakarto datasets containing realworld 3d data from lidar sensors.
Project description
Jakarto datasets for 3d detection challenge of urban assets
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.
Those datasets have been gathered with the 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 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | 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. |
... | ... | ... | ... |
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).
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file jakarto_datasets-0.1.0.tar.gz
.
File metadata
- Download URL: jakarto_datasets-0.1.0.tar.gz
- Upload date:
- Size: 7.0 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2 requests-toolbelt/0.9.1 tqdm/4.40.1 CPython/3.6.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | d8f6b21069f74177077ffb72c1cb6b7f88fa200f5716fdb670f71ac6471e929a |
|
MD5 | cc327e381427863839d6cec97f523097 |
|
BLAKE2b-256 | 138bc9bc78edbdd91a28a586b94a604383c1e938a433cc212d46486dfad457fa |
Provenance
File details
Details for the file jakarto_datasets-0.1.0-py3-none-any.whl
.
File metadata
- Download URL: jakarto_datasets-0.1.0-py3-none-any.whl
- Upload date:
- Size: 9.7 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2 requests-toolbelt/0.9.1 tqdm/4.40.1 CPython/3.6.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | f3f390d15e8e8cd03c985df7535f8bf4c1b077753a7fac784449c48ad474b665 |
|
MD5 | bbd3aaf917416324cf1e7176c6dc505a |
|
BLAKE2b-256 | 8cf0da484c9d79a5a8441008f5f688fb27467e4b71571ca4c8e819a382832a1f |