We explore methods to detect automobiles in Planet imagery and build a large scale vector field for moving objects. Planet operates two distinct constellations: high-resolution SkySat satellites as well as medium-resolution SuperDove satellites. We show that both static and moving cars can be identified reliably in high-resolution SkySat imagery. We are able to estimate the speed and heading of moving vehicles by leveraging the inter-band displacement (or "rainbow" effect) of moving objects. Identifying cars and trucks in medium-resolution SuperDove imagery is far more difficult, though a similar rainbow effect is observed in these satellites and enables moving vehicles to be detected and vectorized. The frequent revisit of Planet satellites enables the categorization of automobile and truck activity patterns over broad areas of interest and lengthy timeframes.

翻译：本文探讨了在行星影像中检测汽车并构建大规模运动物体矢量场的方法。行星公司运营着两个不同的卫星星座：高分辨率的SkySat卫星以及中分辨率的SuperDove卫星。研究表明，在高分辨率SkySat影像中，静态与行驶中的车辆均可被可靠识别。通过利用运动物体在波段间的位移（即“彩虹效应”），我们能够估算出行驶中车辆的速度与航向。在中分辨率SuperDove影像中识别轿车与卡车则更为困难，尽管这些卫星同样观测到类似的彩虹效应，使得运动车辆得以被检测并矢量化。行星卫星的高重访频率使得我们能够对大范围感兴趣区域及长时间跨度内的轿车与卡车活动模式进行分类分析。