The expansion of commercial satellite imagery offers great promise, albeit with an implicit compromise between space and time. Established providers such as DigitalGlobe currently provide exquisite tasked imagery at 0.30–0.50 meter resolution from five satellites. The Space 3.0 company Planet promises daily revisits of 3–5 meter imagery using over 100 satellites, with 1m resolution in certain areas due to their recent acquisition of Google’s Terra Bella constellation. New constellations such as BlackSky promise to provide tasked 1m imagery with revisit rates as high as 40–70 times a day. In essence, these three paradigms occupy vastly different positions in [resolution, revisit, cost] space, which we shall call the satellite utility manifold.

In this post we seek to infer the shape of the manifold along the resolution axis via an approach similar to tomography. We utilize the Cars Overhead with Context (COWC) dataset, which is a large high quality set of annotated cars from overhead imagery. In a previous car localization post we detailed object detection accuracy on this dataset with the YOLT2 framework at 0.30m resolution. In the sections below we quantify the effects of resolution on object detection, with the aim of providing a cross-section of the manifold and informing tradeoffs in satellite design. For our particular dataset we show that objects need only be ~5 pixels in size to be localized with high confidence.

1. The Satellite Utility Manifold

The high revisit rates proposed by Space 3.0 constellations contribute value for many classes of problems, provided resolution remains high enough for the desired task. Many analytical methods still depend on the ability to detect and localize objects of interest, so if objects of interest cannot be detected reliably then no revisit rate can salvage the loss of spatial fidelity. Yet there may exist a sweet spot in the satellite utility manifold where analytics are maximized for a given cost.

The utility and cost of a given constellation design will depend on many factors, though resolution (both spatial and temporal) is a primary driver. For this blog we adopt object detection performance as our measure of utility, though acknowledge that there are multiple possible measures (object detection performance, segmentation accuracy, change detection fidelity, crop cover recall, etc). In the plots below we illustrate the expected morphology of the satellite utility manifold.