@article{,
title= {The Cars Overhead With Context (COWC)},
journal= {},
author= {},
year= {},
url= {http://gdo-datasci.ucllnl.org/cowc/},
abstract= {The Cars Overhead With Context (COWC) data set 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. More information can be obtained by reading our paper here.

The 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.


Data can be downloaded from our FTP server. 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 seen on the right.

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.

![](https://i.imgur.com/0dsvDo0.jpg)},
keywords= {},
terms= {},
license= {},
superseded= {}
}