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.

观察交通流量对当代城市管理具有重要意义。以遥感图像为代表的高空图像提供了有关交通流量的主要信息来源。然而，大多数常见的高分辨率遥感图像的空间分辨率往往被限制在0.5 m甚至更低，这使得通过广泛使用的目标检测方法来统计车辆是不现实的。因此，为了探索遥感数据在研究全球城市发展和管理方面的潜力，本文介绍了一种基于密度图的有限分辨率遥感图像车辆计数方法。基于密度图的模型将车辆计数任务视为根据像素值估计车辆目标的密度。我们提出了一个改进的基于 CNN 的网络，^— )，从输入的遥感图像生成车辆密度图。还引入了一个新的数据集 RSVC2021，它是从公共 DOTA 和 ITCVD 数据集生成的，用于网络训练和测试。相应地建立了 RSVC2021 数据集的基准，并且 CSRNet ^—被选为后续实验的基线模型。在覆盖武汉市的 COVID-19 疫情封锁之前、期间和之后拍摄的一组分辨率为 1 m 的 GF-2 时间序列图像用于实际应用测试。RSVC2021数据集和真实卫星图像的测试结果证实，在计数值和可视化密度图方面，所提出的方法均取得了良好的性能，并在该任务中表现出相当大的应用潜力。RSVC2021 数据集的生成代码将在 https://github.com/YinongGuo/RSVC2021-Dataset 公开。