Assessment of traffic congestion with high-resolution remote sensing data and deep convolution neural network

Abstract

In this study, high resolution (HR) images, GIS and deep convolution neural network (DCNN) are used for assessment of traffic congestion. A DCNN architecture comprises one convolution layer, two pooling layers and a five-layer fully connected neural network (FCNN) evaluated for identifying vehicles in a movable window in HR images. A simple mathematical method is followed for changing the scale and orientation of the movable window to optimally mask and measure the area of vehicles. A formula is appraised to compute the traffic density using the estimated vehicles and road areas. A threshold is used to estimate traffic congestion from the measured traffic density. The method is validated by applying on World View-2 pan-sharpened multispectral images having spatial resolution 0.46 m. In comparison to CNN and ResNet-18, the proposed approach achieves a quite promising accuracy (99%) and needs less training and processing time for measuring traffic congestion in HR images.