# Visual search over billions of aerial and satellite images

arXiv: https://arxiv.org/abs/2002.02624 · HTML: https://mlsanity.com/paper/2002.02624/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [METER-ML: A Multi-Sensor Earth Observation Benchmark for Automated Methane Source Mapping](https://mlsanity.com/paper/2207.11166/) (2022)
- [Representation Learning for Remote Sensing: An Unsupervised Sensor Fusion Approach](https://mlsanity.com/paper/2108.05094/) (2021)
- [RapidAI4EO: A Corpus for Higher Spatial and Temporal Reasoning](https://mlsanity.com/paper/2110.01919/) (2021)

## Abstract (arXiv)

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.