DVM-CAR: A large-scale automotive dataset for visual marketing research and applications
This dataset addresses a data scarcity problem for researchers and analysts in the automotive industry and related fields, though it is incremental as it focuses on data collection rather than novel methods.
The authors tackled the lack of large-scale data for product aesthetics analytics by creating DVM-CAR, a comprehensive automotive dataset with 1.4 million images from 899 car models, along with specifications and sales information over ten years in the UK market.
There is a growing interest in product aesthetics analytics and design. However, the lack of available large-scale data that covers various variables and information is one of the biggest challenges faced by analysts and researchers. In this paper, we present our multidisciplinary initiative of developing a comprehensive automotive dataset from different online sources and formats. Specifically, the created dataset contains 1.4 million images from 899 car models and their corresponding model specifications and sales information over more than ten years in the UK market. Our work makes significant contributions to: (i) research and applications in the automotive industry; (ii) big data creation and sharing; (iii) database design; and (iv) data fusion. Apart from our motivation, technical details and data structure, we further present three simple examples to demonstrate how our data can be used in business research and applications.