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Use AI To Convert Ancient Maps Into Satellite-Like Images

Updated maps could show land use changes over time, including the social and economic impacts of urbanization

2 min read
Left: A map created in 1808 by José Fernandes Portugal, which portrays the landscape around what is modern day Recife, in Brazil. Right: A image generated using AI, which was created based on the 1808 map of Recife.
A map created in 1808 by José Fernandes Portugal, which portrays the landscape around what is modern day Recife, in Brazil (left). The map was the basis for this image (right) generated using AI.
Images: Escola Politécnica da Universidade de Pernambuco/IEEE

Ancient maps give us a slight glimpse of how landscapes looked like centuries ago. But what would we see if we looked at these older maps with a modern lens?

Henrique Andrade is a student at Escola Politécnica da Universidade de Pernambuco, Recife who has been studying maps of his hometown Recife, in Brazil, for several years now. “I gathered all these digital copies of maps, and I ended up discovering things about my hometown that aren't so widely known,” he says. “I feel that in Recife people were denied access to their own past, which makes it difficult for them to understand who they are, and consequently what they can do about their own future.”

Andrade approached a professor at his university, Bruno Fernandes, with an idea: to develop a machine learning algorithm that could transform old maps into Google satellite images. Such an approach, he believes, could inform people of how land use has changed over time, including the social and economic impacts of urbanization.

To see the project realized, they used an existing AI tool called Pix2pix, which relies on two neural networks. The first one creates images based on the input set, while the second network that decides if the generated image is fake or not. The networks are then trained to fool each other, and ultimately create realistic-looking images based on the historical data provided.

Andrade and Fernandes describe their approach in a study published 24 September 2020 in IEEE Geoscience and Remote Sensing Letters. In this study, they took a map of Recife from 1808 and generated modern day images of the area.

An AI-generated image of the region around Recife in 1808, in contrast to a real satellite image of the area taken in 2020.An AI-generated image of the region around Recife in 1808, in contrast to a real satellite image of the area taken in 2020 (right).Images: Escola Politécnica da Universidade de Pernambuco/IEEE

“When you look at the images, you get a better grasp of how the city has changed in 200 years,” explains Andrade. “The city's geography has drastically changed—landfills have reduced the water bodies and green areas were all removed by human activity.”

He says an advantage of this AI approach is that it requires relatively little input volume; however, the input requires some historical context, and the resolution of the generated images is lower than what the researchers would like.

“Moving forward, we are working on improving the resolution of the images, and experimenting on different inputs,” says Andrade. He sees this approach to generate modern images of the past as widely applicable, noting that it could be applied to various locations and could be used by urban planners, anthropologists, and historians.

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Will AI Steal Submarines’ Stealth?

Better detection will make the oceans transparent—and perhaps doom mutually assured destruction

11 min read
A photo of a submarine in the water under a partly cloudy sky.

The Virginia-class fast attack submarine USS Virginia cruises through the Mediterranean in 2010. Back then, it could effectively disappear just by diving.

U.S. Navy

Submarines are valued primarily for their ability to hide. The assurance that submarines would likely survive the first missile strike in a nuclear war and thus be able to respond by launching missiles in a second strike is key to the strategy of deterrence known as mutually assured destruction. Any new technology that might render the oceans effectively transparent, making it trivial to spot lurking submarines, could thus undermine the peace of the world. For nearly a century, naval engineers have striven to develop ever-faster, ever-quieter submarines. But they have worked just as hard at advancing a wide array of radar, sonar, and other technologies designed to detect, target, and eliminate enemy submarines.

The balance seemed to turn with the emergence of nuclear-powered submarines in the early 1960s. In a 2015 study for the Center for Strategic and Budgetary Assessment, Bryan Clark, a naval specialist now at the Hudson Institute, noted that the ability of these boats to remain submerged for long periods of time made them “nearly impossible to find with radar and active sonar.” But even these stealthy submarines produce subtle, very-low-frequency noises that can be picked up from far away by networks of acoustic hydrophone arrays mounted to the seafloor.

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