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arXiv · 2002.02624

Visual search over billions of aerial and satellite images

Ryan Keisler·Samuel W. Skillman·Sunny Gonnabathula·Justin Poehnelt·Xander Rudelis·Michael S. Warren
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Abstract

We present a system for performing visual search over billions of aerial and satellite images. The purpose of visual search is to find images that are visually similar to a query image. We define visual similarity using 512 abstract visual features generated by a convolutional neural network that has been trained on aerial and satellite imagery. The features are converted to binary values to reduce data and compute requirements. We employ a hash-based search using Bigtable, a scalable database service from Google Cloud. Searching the continental United States at 1-meter pixel resolution, corresponding to approximately 2 billion images, takes approximately 0.1 seconds. This system enables real-time visual search over the surface of the earth, and an interactive demo is available at https://search.descarteslabs.com.

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Ryan Keisler, Samuel W. Skillman, Sunny Gonnabathula, Justin Poehnelt, Xander Rudelis, Michael S. Warren. 2020-02-07. Visual search over billions of aerial and satellite images. https://doi.org/10.1016/j.cviu.2019.07.010

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