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

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.