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Cities are scrambling to prevent flooding

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Cities are scrambling to prevent flooding


Urban centers are more prone to flooding than other areas because streets, parking lots, and buildings are impervious, meaning water can’t seep into the ground the way it would in a forest or grassland. Instead, it flows.

Detroit, like many older cities, deals with flowing stormwater by combining it with sewage. This blend is then pumped to treatment plants. During the recent storm, electrical outages and mechanical issues knocked out four of 12 pumps in two major pump stations.

The agency has spent $10 million over the past several years upgrading just these two pump stations, and hundreds of millions more on other improvements. But fully modernizing the sewer system would require building a separate stormwater network at a cost of over $17 billion.

Stormwater infrastructure around the country is aging, and many governments have resorted to Band-Aid solutions instead of building more resilient systems, says Mikhail Chester, an infrastructure and policy researcher at Arizona State University. And mechanical and electrical systems are bound to fail occasionally during major storms, Chester adds.

However, even if the pump stations had worked perfectly, they might not have prevented disastrous flooding.

Outdated models

Detroit’s pumping stations, similar to a lot of stormwater infrastructure, were designed to keep up with a 10-year storm, meaning an amount of rainfall within an hour that has roughly a one in 10 chance of happening in any given year. A 10-year storm in the Detroit area would amount to about 1.7 inches of rainfall in an hour, according to National Weather Service data.

During the June storm, parts of Detroit saw intense levels of rainfall that would be more characteristic of a 1,000-year storm (over 3.7 inches of rain within an hour), far beyond the capacity of the pumping stations, according to the water authority.

But rainfall predictions are based on historical data that might not represent the true odds of major storms, according to Anne Jefferson, a hydrologist at Kent State University. Storms that supposedly have a one in 10 chance of happening in any given year are likely happening more often now because of climate change. And she says few agencies are taking climate change into account in their infrastructure designs.

“We’re locking ourselves into a past climate,” Jefferson says.

Governments hoping to account for climate change when designing infrastructure face uncertainty—should they plan for the best-case emissions scenarios or the worst? And how exactly emissions will affect rainfall is difficult to predict.

Planning for bigger storms is an admirable goal, but it’s also costly. Bigger pumps and pipes are more expensive to build and harder to install, says Chester. And price increases aren’t linear, he adds—a pump or pipe with double the capacity will be more than double the price in most cases.

Fast forward

Coastal cities face even more dire climate threats, and some are investing aggressively to stave them off. Tampa, Florida, spent $27 million upgrading pump stations and other infrastructure after major floods in 2015 and 2016, according to the Tampa Bay Times. Some of the upgrades appear to be working—this year at least, the city avoided floods during major storms like Hurricane Elsa.

However, the rising seas along Tampa’s shoreline may soon cover up the pumps’ outlets. If sea levels reach the spot where water is supposed to exit storm pipes, the system won’t be able to remove water from the city.

Some cities are looking to install other features, like storm ponds and rain gardens, to help manage urban flooding. Grassy areas like rain gardens can reduce the volume and speed of excess water, Jefferson says. If enough of these facilities are built in the right places, they can help prevent smaller floods, she adds, but like other stormwater infrastructure, they’re usually not designed to stop flooding during larger storms.

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The Download: metaverse fashion, and looser covid rules in China

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The Download: metaverse fashion, and looser covid rules in China


Fashion creator Jenni Svoboda is designing a beanie with a melted cupcake top, sprinkles, and doughnuts for ears. But this outlandish accessory isn’t destined for the physical world—Svoboda is designing for the metaverse. She’s working in a burgeoning, if bizarre, new niche: fashion stylists who create or curate outfits for people in virtual spaces.

Metaverse stylists are increasingly sought-after as frequent users seek help dressing their avatars—often in experimental, wildly creative looks that defy personal expectations, societal standards, and sometimes even physics. 

Stylists like Svoboda are among those shaping the metaverse fashion industry, which is already generating hundreds of millions of dollars. But while, to the casual observer, it can seem outlandish and even obscene to spend so much money on virtual clothes, there are deeper, more personal, reasons why people are hiring professionals to curate their virtual outfits. Read the full story.

—Tanya Basu

Making sense of the changes to China’s zero-covid policy

On December 1, 2019, the first known covid-19 patient started showing symptoms in Wuhan. Three years later, China is the last country in the world holding on to strict pandemic control restrictions. However, after days of intense protests that shocked the world, it looks as if things could finally change.

Beijing has just announced wide-ranging relaxations of its zero covid policy, including allowing people to quarantine at home instead of in special facilities for the first time.

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Uber’s facial recognition is locking Indian drivers out of their accounts 

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Uber checks that a driver’s face matches what the company has on file through a program called “Real-Time ID Check.” It was rolled out in the US in 2016, in India in 2017, and then in other markets. “This prevents fraud and protects drivers’ accounts from being compromised. It also protects riders by building another layer of accountability into the app to ensure the right person is behind the wheel,” Joe Sullivan, Uber’s chief security officer, said in a statement in 2017.

But the company’s driver verification procedures are far from seamless. Adnan Taqi, an Uber driver in Mumbai, ran into trouble with it when the app prompted him to take a selfie around dusk. He was locked out for 48 hours, a big dent in his work schedule—he says he drives 18 hours straight, sometimes as much as 24 hours, to be able to make a living. Days later, he took a selfie that locked him out of his account again, this time for a whole week. That time, Taqi suspects, it came down to hair: “I hadn’t shaved for a few days and my hair had also grown out a bit,” he says. 

More than a dozen drivers interviewed for this story detailed instances of having to find better lighting to avoid being locked out of their Uber accounts. “Whenever Uber asks for a selfie in the evenings or at night, I’ve had to pull over and go under a streetlight to click a clear picture—otherwise there are chances of getting rejected,” said Santosh Kumar, an Uber driver from Hyderabad. 

Others have struggled with scratches on their cameras and low-budget smartphones. The problem isn’t unique to Uber. Drivers with Ola, which is backed by SoftBank, face similar issues. 

Some of these struggles can be explained by natural limitations in face recognition technology. The software starts by converting your face into a set of points, explains Jernej Kavka, an independent technology consultant with access to Microsoft’s Face API, which is what Uber uses to power Real-Time ID Check. 

Adnan Taqi holds up his phone in the driver’s seat of his car. Variations in lighting and facial hair have likely caused him to lose access to the app.

SELVAPRAKASH LAKSHMANAN

“With excessive facial hair, the points change and it may not recognize where the chin is,” Kavka says. The same thing happens when there is low lighting or the phone’s camera doesn’t have a good contrast. “This makes it difficult for the computer to detect edges,” he explains.

But the software may be especially brittle in India. In December 2021, tech policy researchers Smriti Parsheera (a fellow with the CyberBRICS project) and Gaurav Jain (an economist with the International Finance Corporation) posted a preprint paper that audited four commercial facial processing tools—Amazon’s Rekognition, Microsoft Azure’s Face, Face++, and FaceX—for their performance on Indian faces. When the software was applied to a database of 32,184 election candidates, Microsoft’s Face failed to even detect the presence of a face in more than 1,000 images, throwing an error rate of more than 3%—the worst among the four. 

It could be that the Uber app is failing drivers because its software was not trained on a diverse range of Indian faces, Parsheera says. But she says there may be other issues at play as well. “There could be a number of other contributing factors like lighting, angle, effects of aging, etc.,” she explained in writing. “But the lack of transparency surrounding the use of such systems makes it hard to provide a more concrete explanation.” 

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The Download: Uber’s flawed facial recognition, and police drones

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The Download: Uber’s flawed facial recognition, and police drones


One evening in February last year, a 23-year-old Uber driver named Niradi Srikanth was getting ready to start another shift, ferrying passengers around the south Indian city of Hyderabad. He pointed the phone at his face to take a selfie to verify his identity. The process usually worked seamlessly. But this time he was unable to log in.

Srikanth suspected it was because he had recently shaved his head. After further attempts to log in were rejected, Uber informed him that his account had been blocked. He is not alone. In a survey conducted by MIT Technology Review of 150 Uber drivers in the country, almost half had been either temporarily or permanently locked out of their accounts because of problems with their selfie.

Hundreds of thousands of India’s gig economy workers are at the mercy of facial recognition technology, with few legal, policy or regulatory protections. For workers like Srikanth, getting blocked from or kicked off a platform can have devastating consequences. Read the full story.

—Varsha Bansal

I met a police drone in VR—and hated it

Police departments across the world are embracing drones, deploying them for everything from surveillance and intelligence gathering to even chasing criminals. Yet none of them seem to be trying to find out how encounters with drones leave people feeling—or whether the technology will help or hinder policing work.

A team from University College London and the London School of Economics is filling in the gaps, studying how people react when meeting police drones in virtual reality, and whether they come away feeling more or less trusting of the police. 

MIT Technology Review’s Melissa Heikkilä came away from her encounter with a VR police drone feeling unnerved. If others feel the same way, the big question is whether these drones are effective tools for policing in the first place. Read the full story.

Melissa’s story is from The Algorithm, her weekly newsletter covering AI and its effects on society. Sign up to receive it in your inbox every Monday.

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