Posted on April 18, 2018
From retail to manufacturing, images taken from space can provide useful information about markets. However, before machine analysis can produce insights for you, you may have to address a few technical challenges.
Satellite image providers tend to showcase their best images, which should be relatively easy to analyze:
Various methods for analyzing of high quality, high-resolution overhead images for specific tasks, such as vehicle detection, have been covered extensively. A great example of such projects is “Cars Overhead with Context” (COWC). Their set contains 32,716 unique annotated cars and 58,247 unique negative examples from different geographical location and produced by different image providers. We tested this model on RS Metrics retail data, and found it to work well (90% detection rate) on high-quality images such as these:
However, you often receive images that don’t look like any of the samples above. Picture quality may be affected by clouds, lighting, positioning, even processing errors.
Data quality This is an uncompressed image of a Bed Bath & Beyond parking lot, clipped from an actual satellite photo from one of the top data providers:
For some applications (e.g. analysis of crops, large construction projects), the spatial quality problem may be alleviated through high revisit rates — as “missing” data can be interpolated from multiple images.
I’ve seen several blog articles that emulate bad quality and low resolution by applying gaussian blur to high-quality images, before testing the effectiveness of ML models such as “COWC” on them, and drawing conclusions about their effectiveness. A model may perform reasonably well in such situations, but fail when faced with the kind of defects and artifacts that are often present in real-world commercial satellite data.
There’s more! Read the full blog post on Medium
Airbus Telecommunications Satellites
Originally published by Michael Alatortsev at www.alatortsev.com on April 17, 2018.