Mundhenk, T. Nathan; Konjevod, Goran; Sakla, Wesam A.; Boakye, Kofi (2020). Cars Overhead with Context (COWC). In Lawrence Livermore National Laboratory (LNLL) Open Data Initiative. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0CN72BC

The Cars Overhead With Context (COWC) dataset is a large set of annotated cars from overhead. It is useful for training a device such as a deep neural network to learn to detect and/or count cars.

The COWC dataset has the following attributes:
1. Data from overhead at 15 cm per pixel resolution at ground (all data is EO).
2. Data from six distinct locations: Toronto Canada, Selwyn New Zealand, Potsdam and Vaihingen Germany, Columbus and Utah United States.
3. 32,716 unique annotated cars. 58,247 unique negative examples.
4. Intentional selection of hard negative examples.
5. Established baseline for detection and counting tasks.
6. Extra testing scenes for use after validation.

The data includes wide area imagery with annotations as well as precompiled image sets for training/validation of classification and counting. Examples of the precompiled image sets are provided.

A newer subset (COWC-M) also differentiates between four different types of automobiles.

a) Sedan
b) Pickup
c) Other
d) Unknown

• 2015-07

• 2020

• Boakye, Kofi
• Konjevod, Goran
• Mundhenk, T. Nathan
• Sakla, Wesam A.

• Lawrence Livermore National Laboratory (LLNL)

The dataset and research to create this data was done by members of the Computer Vision group within the Computation Engineering Division at Lawrence Livermore National Laboratory under grant from NA-22 in the Global Security Directorate. No Llamas were harmed in the creation of this set.

• Automobile Classification
• Cars
• Cars Overhead with Context (COWC)
• Context
• Convolutional Neural Networks (CNN)
• Counting
• Deep learning
• Detection

Doi: https://doi.org/10.6075/J0CN72BC

Primary associated publication

• Mundhenk T.N., Konjevod G., Sakla W.A., Boakye K. (2016) A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9907. Springer, Cham. https://doi.org/10.1007/978-3-319-46487-9_48

Software

• GitHub repository: https://github.com/LLNL/cowc

Other resource

• Columbus Surrogate Unmanned Aerial Vehicle (CSUAV) data: https://www.sdms.afrl.af.mil/index.php?collection=csuav
• International Society for Photogrammetry and Remote Sensing (ISPRS) Test Project on Urban Classification, 3D Building Reconstruction and Semantic Labeling: http://www2.isprs.org/commissions/comm3/wg4/tests.html
• Land Information New Zealand (LINZ) data: http://www.linz.govt.nz/
• Project web page: https://gdo152.llnl.gov/cowc/
• Utah Automated Geographic Reference Center (AGRC) High Resolution Orthophotography (HRO) data: http://gis.utah.gov/data/aerial-photography/

GNU AFFERO GENERAL PUBLIC LICENSE Version 3, 19 November 2007

• Lawrence Livermore National Laboratory

Under copyright (US)

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2023-06-06