  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.
