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Vehicle Detection in Satellite Images with Deep Neural Networks and Vehicle Shape Features

The vehicle geographical distribution information including vehicle location and quantity is essential for planning the future power network capacity as all the conventional vehicles will be replaced by the electric vehicles (EVs) in the next few decades. However, collecting such information manually is expensive and time-consuming. With the development of computer vision techniques, it brings an opportunity to build an intelligent detector that can detect vehicles in satellite images automatically, which can help the power energy suppliers gather vehicle information more efficiently. In this paper, two convolutional neural networks (CNN), SegNet and Mask R-CNN, are employed for identifying vehicles from satellite images. The CNN results are further improved by support vector machine (SVM) with vehicle shape features. The real satellite images covering central Manchester area are used to demonstrate the effectiveness of the proposed methods.