Vitenskapelig artikkel   2021

Reksten, Jarle Hamar; Salberg, Arnt-Børre



International Journal of Remote Sensing, vol. 42, p. 865–883, 2021


Taylor & Francis



Internasjonale standardnumre:

Trykt: 0143-1161
Elektronisk: 1366-5901



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.