ABSTRACT
Observing traffic flow is of great significance to contemporary urban management. Overhead images, as represented by remote sensing images, provide a major source of information about traffic flow. However, the spatial resolutions of most common high-resolution remote sensing images are often limited to 0.5 m and even below, which makes it unrealistic to count vehicles by means of widely used object detection methods. Therefore, to explore the potential of remote sensing data for studying global urban development and management, this paper introduces a density map-based vehicle counting method for remote sensing imagery with limited resolution. Density map-based models regard the vehicle counting task as estimating the density of vehicle targets in terms of pixel values. We propose an improved CNN-based network, called Congested Scene Recognition Network Minus (CSRNet—), that generates a density map of vehicles from the input remote sensing imagery. A new dataset, RSVC2021, which was generated from the public DOTA and ITCVD datasets, is also introduced for network training and testing. A benchmark on the RSVC2021 dataset is accordingly established and CSRNet— is selected as the baseline model for subsequent experiments. A set of GF-2 time series images with a resolution of 1 m taken before, during and after the COVID-19 epidemic lockdown covering Wuhan city are applied for real-world application testing. The testing results on both the RSVC2021 dataset and real satellite images confirm that, in terms of both the counting values and the visualized density maps, the proposed method achieves good performance and exhibits considerable application potential in this task. The generating codes of RSVC2021 dataset will be publicly available at https://github.com/YinongGuo/RSVC2021-Dataset.
ABSTRACT
Manual vehicle counting is often tedious, expensive, and time-consuming. While automatic counting from CCTV allows for annual average daily traffic estimation, CCTV files in the Philippines are not available to the public and do not fully cover all road extents. In this study, Remote Sensing and Geographic Information Systems (GIS) techniques are employed to use readily available satellite images to obtain vehicle count in selected road segments in the Central Business Districts of Quezon City before and after the COVID-19 lockdown. Using the existing Google Earth Images, a segmentation algorithm using ENVI Feature Classification was developed to allow remote counting of vehicles from the earliest image in 2018. The devised algorithm was able to delineate, identify, and classify according to the types of vehicles that are visible on the image. An average error rate of 12.24% was found by comparison of automated counts and manual counts on the images, while a regression analysis yielded a value of R2 Combining double low line 0.9227 that denoted a strong relationship between automated and manual counts. Vehicle density was calculated, and percent differences were obtained to determine the relative differences of the vehicle counts from the vehicle count of the earliest image taken in 2018. It was found that the vehicle density declined by at least 81% by March 25, 2020. The methodological framework presented in this study provides estimates of vehicle counts and vehicle density. It can be further improved if vehicle counts, on the same location and period, from field validation surveys are available. © International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives