Cars Overhead with Context (COWC). In Lawrence Livermore National Laboratory (LLNL) Open Data Initiative

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