## 1 Introduction  
Video satellites that emerged in recent years have provided a new way to observe the Earth. Their high spatial resolution (commonly less than 1 m) and near-continuous temporal resolution make it possible for humans to observe the dynamic changes of smaller objects in a wider field of view [1]. Achieving this capability is challenging with conventional static satellites. The leap from static observation to dynamic monitoring represents a huge transformation, and is of great significance for real-time monitoring [2]. Video satellites have attracted significant globe attention. At the same time, many countries have begun to build a huge satellite constellation surrounding the Earth, with a series of small and low-cost satellites. The combination of video satellites and constellations will greatly improve the observation capability of space-based remote sensing. This provides an unprecedented opportunity for the application of video satellites in transportation land planning and management. A large amount of effort has been put into traffic information extraction using satellite videos, since the launch of the first video satellite Skysat [3]. At present, transportation land planning and management heavily relies on the traffic data obtained from fixed or mobile detectors on the ground [4]. The fixed detectors,such as sensing coils, video cameras, and microwave sensors on the roads, have the disadvantage of requiring labor-intensive installation. Obtaining traffic information in this way is challenging in remote areas with limited infrastructure. Mobile detectors, such as GPS-based probe vehicles and electronic map operators, face issues of insufficient data coverage and quality. Satellite video provides a near-continuous and wide field of view, overcoming many shortcomings of ground-based data acquisition methods. The key to applying satellite videos for traffic parameter estimation is the accurate detection of moving vehicles, which is also the foundation of transportation planning and management. However, this is a very challenging task due to the following factors. First, vehicles in satellite videos are very small, occupying only several pixels. The shape and texture features of vehicles are unclear and easily confused with noise [5]. Second, there is complex relative motion between satellites and the Earth. Video satellites adopt the \"staring\" imaging mode to observe a specific area. On the one hand, this leads to the so-called dynamic scenario in videos, namely, the whole scenario presents sub-pixel level and uneven motion. On the other hand, it causes parallax pseudo motion, where objects appear to move in the video, but are stationary in reality, such as buildings or towers. Third, vehicles are easily obscured by trees, bridges and shadows due to the satellite's bird's-eye view. The occlusion of vehicles in specific frames can decrease the accuracy of traffic flow parameter estimation. We present a review of some commonly used moving vehicle detection and tracking methods in satellite video. Most of these methods are derived from the field of computer vision. 1. Moving vehicle detection methods Moving vehicle detection aims to accurately identify moving vehicles from a satellite video scenario. The commonly used approaches are typically divided into three categories--frame difference, optical flow and background subtraction. Frame difference methods use adjacent frames to detect moving objects based on their differences [6]. Although it is simple and easy to use, the obvious problem here lies in its inability to effectively process complex background features. Optical flow can determine the instantaneous velocity of a moving object in the imaging plane [7]. It has rigorous logic, but the assumptions are often not satisfied in real applications, which leads to poor results. The background subtraction method aims to model a stable, accurate background [8]. The foreground can then be obtained by subtracting the modeled background from a frame.