Datasets:

Zenseact
/

ZOD

	---
	license: cc-by-sa-4.0
	task_categories:
	- object-detection
	- image-classification
	- depth-estimation
	- image-segmentation
	language:
	- en
	tags:
	- Autonomous Driving
	- Autonomous Vehicles
	- Images
	- Lidar
	- GNSS/IMU
	- Vehicle Data
	- Satellite Positioning
	pretty_name: ZOD
	size_categories:
	- 10K<n<100K
	paperswithcode_id: zenseact-open-dataset
	---
	# Dataset Card for ZOD

	The Zenseact Open Dataset (ZOD) is a large multi-modal autonomous driving (AD) dataset created by researchers at Zenseact. It was collected over a 2-year period in 14 different European counties, using a fleet of vehicles equipped with a full sensor suite. The dataset consists of three subsets: Frames, Sequences, and Drives, designed to encompass both data diversity and support for spatiotemporal learning, sensor fusion, localization, and mapping. Together with the data, we have developed a SDK containing tutorials, downloading functionality, and a dataset API for easy access to the data. The development kit is available on Github.

	## Dataset Details

	### Dataset Description
	ZOD is a large-scale diverse, multimodal AD dataset, collected over two years in various European countries.
	It has highest-range and resoutions sensors and contains data from various traffic scenarios.

	- Curated by: Zenseact AB
	- Funded by: Zenseact AB
	- Shared by: Zenseact AB
	- Language(s): English
	- License: CC BY-SA

	### Dataset Sources [optional]

	- Repository: https://github.com/zenseact/zod
	- Paper: https://arxiv.org/abs/2305.02008
	- Website: https://zod.zenseact.com/

	## Uses

	<!-- Address questions around how the dataset is intended to be used. -->

	### Direct Use

	<!-- This section describes suitable use cases for the dataset. -->

	[More Information Needed]

	### Out-of-Scope Use

	<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

	[More Information Needed]

	## Dataset Structure

	<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

	[More Information Needed]

	## Dataset Creation

	### Curation Rationale

	<!-- Motivation for the creation of this dataset. -->

	[More Information Needed]

	### Source Data

	<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

	#### Data Collection and Processing

	<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

	[More Information Needed]

	#### Who are the source data producers
	The Zenseact Open Dataset (ZOD) is the property of Zenseact AB (© 2022 Zenseact AB), and is collected by several developmental vehicles with an identical sensor layout.

	### Annotations [optional]

	<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

	#### Annotation process

	<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

	[More Information Needed]


	#### Personal and Sensitive Information
	To protect the privacy of every individual in our dataset, and to comply with privacy regulations such as the European Union’s General Data Protection Regulation (GDPR), we employ third-party services (Brighter AI) to anonymize all images in our dataset. The anonymization should protect all personally identifiable information in the images, including faces and license plates.

	For Frames we supply two types of anonymization, namely Deep Neural Anonymization Technology (DNAT) and blurring. We studied the effects that these two anonymization methods have on downstream computer vision tasks and found no significant difference between the two. For more details about the experiments, see our paper. After this study, we anonymized the Sequences and Drives using the blurring anonymization method only.

	## Bias, Risks, and Limitations

	<!-- This section is meant to convey both technical and sociotechnical limitations. -->

	[More Information Needed]

	### Recommendations

	<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

	Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

	## Citation [optional]
	@inproceedings{alibeigi2023zenseact,
	title={Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous driving},
	author={Alibeigi, Mina and Ljungbergh, William and Tonderski, Adam and Hess, Georg and Lilja, Adam and Lindstrom, Carl and Motorniuk, Daria and Fu, Junsheng and Widahl, Jenny and Petersson, Christoffer},
	booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
	year={2023}
	}

	## Glossary

	ZOD stands for Zenseact Open Dataset.
	AD stands for Autonomous Driving.

	## Dataset Card Authors [optional]

	[More Information Needed]

	## Dataset Card Contact

	opendataset@zenseact.com