Visual Search Over Billions Of Aerial And Satellite Images
Ryan Keisler, Samuel W. Skillman, Sunny Gonnabathula, Justin Poehnelt, Xander Rudelis, Michael S. Warren . Computer Vision and Image Understanding 2020 – 23 citations
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[Paper]
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.