Vehicle Vectors and Traffic Patterns from Planet Imagery

TL;DR

This paper presents methods to detect and analyze moving vehicles in Planet satellite imagery, enabling large-scale traffic pattern analysis by leveraging high- and medium-resolution data and the rainbow effect for velocity estimation.

Contribution

It introduces techniques for identifying static and moving vehicles in satellite images and estimates their speed and direction using the rainbow effect, applicable to multiple satellite resolutions.

Findings

Reliable detection of cars and trucks in high-resolution imagery.

Velocity and heading estimation of moving vehicles using rainbow effect.

Feasibility of broad-area traffic pattern analysis over time.

Abstract

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.