Estimating Traffic in Urban Areas from Very-High Resolution Aerial Images

Abstract

Traffic estimation from very-high-resolution remote-sensing imagery has received increasing interest during the last few years. In this article, we propose an automatic system for estimation of the annual average daily traffic (AADT) using very-high-resolution optical remote-sensing imagery of urban areas in combination with high-quality, but very spatially limited, ground-based measurements. The main part of the system is the vehicle detection, which is based on the deep learning object detection architecture mask region-based convolutional neural network (Mask R-CNN), modified with an image normalization strategy to make it more robust for test images of various conditions and the use of a precise road mask to assist the filtering of driving vehicles from parked ones. Furthermore, to include the high-quality ground-based measurements and to make the traffic estimates more consistent across neighbouring road links, we propose a graph smoothing strategy that utilizes the road network. The fully automatic processing chain has been validated on a set of aerial images covering the city of Narvik, Norway. The precision and recall rate of detecting driving vehicles was 0.74 and 0.66, respectively, and the AADT was estimated with a root mean squared error (RMSE) of 2279 and bias of −383. We conclude that separating driving vehicles from parked ones may be challenging if vehicles are parked along the roads and that for urban environment with short road links several remotesensing images covering the road links at different time instances are necessary in order to benefit from the remote-sensing images.