用卫星影像实现大范围车辆检测与速度估算，支持全球交通动态监测。
Deep Learning Enhanced Road Traffic Analysis: Scalable Vehicle Detection and Velocity Estimation Using PlanetScope Imagery
- 基于关键点R-CNN模型，利用多波段时间差追踪车辆轨迹。
- 检测mAP达0.53，速度误差约3.4 m/s，优于传统方法覆盖范围。
- 适合需要大规模、低成本交通监测的机构或研究者使用。
本文提出一种基于PlanetScope SuperDove卫星影像的车辆检测与速度估计方法，为全球交通监控提供可扩展解决方案。传统方式如固定传感器和无人机受限于覆盖范围、成本及法律限制。卫星方法虽具广域覆盖优势，但面临高成本、低帧率及小目标检测难等问题。本文采用关键点R-CNN模型，通过多光谱波段的时间差异追踪车辆轨迹并估算速度。验证基于德国与波兰高速公路的无人机影像与GPS数据进行。模型达到0.53的均值平均精度（mAP），速度估计误差约为3.4 m/s。与无人机数据对比显示，卫星数据平均速度为112.85 km/h，低于无人机测得的131.83 km/h，表明高速场景仍存偏差。尽管如此，该方法已展现出在广阔区域实现每日交通监测的潜力，为理解全球交通动态提供重要支持。
原文摘要 · Abstract (English)
This paper presents a method for detecting and estimating vehicle speeds using PlanetScope SuperDove satellite imagery, offering a scalable solution for global vehicle traffic monitoring. Conventional methods such as stationary sensors and mobile systems like UAVs are limited in coverage and constrained by high costs and legal restrictions. Satellite-based approaches provide broad spatial coverage but face challenges, including high costs, low frame rates, and difficulty detecting small vehicles in high-resolution imagery. We propose a Keypoint R-CNN model to track vehicle trajectories across RGB bands, leveraging band timing differences to estimate speed. Validation is performed using drone footage and GPS data covering highways in Germany and Poland. Our model achieved a Mean Average Precision of 0.53 and velocity estimation errors of approximately 3.4 m/s compared to GPS data. Results from drone comparison reveal underestimations, with average speeds of 112.85 km/h for satellite data versus 131.83 km/h from drone footage. While challenges remain with high-speed accuracy, this approach demonstrates the potential for scalable, daily traffic monitoring across vast areas, providing valuable insights into global traffic dynamics.
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