Australia’s fire season looms and researchers are looking to move beyond ‘detecting fires with people in towers and binoculars’.
4 min
SYDNEY—In October, a sensor mounted on an 8.5-metre pole in the Australian countryside detected small particles in the air near a timber plantation and sent out an alert.
When plantation staff arrived, they found a small man-made fire that was already under control, but the incident offered a glimpse of how authorities hope to use new technology to battle bushfires that have grown increasingly intense in recent years.
As a new fire season begins in Australia—a recent bushfire ravaged about half of Fraser Island in the country’s east—researchers are looking to rely more on technology to find blazes quickly and better predict their path.
A monthslong government inquiry into the devastating 2019-2020 fire season in Australia, which killed more than two dozen people and burned an area bigger than Washington state, concluded that authorities need to be better prepared and recommended that officials work with the private sector to develop new technology.
“We’re still detecting fires with people in towers and binoculars. We’ve got to move beyond that,” said Leigh Kelson, program director at FireTech Connect in Australia, a government-funded effort to help startups develop new firefighting technologies. Firefighters also rely on the equivalent of 911 calls to find new blazes.
The ideas researchers are exploring include fitting drones with lasers that can map dry areas at higher fire risk and whether satellites can detect extreme fire behaviour.
Last fire season, authorities were surprised by how quickly the blazes spread and how long they burned, which they later attributed to a prolonged drought that had left the land parched with plenty of dry vegetation to fuel the flames. Last year was Australia’s hottest year on record, according to the government’s Bureau of Meteorology, and fires are projected to become more intense and frequent because of climate change.
Parts of Australia have received more rainfall in recent months, and the current fire season isn’t expected to be as severe. Still, firefighters have struggled to control some of the blazes. The fire on Fraser Island, the largest sand island in the world and a Unesco World Heritage site, prompted officials to tell some residents to evacuate from homes in the path of the flames. On Sunday, firefighters said rainfall had finally helped to contain that blaze, which started in mid-October.
Other regions, including the western U.S. and even in frosty Siberia, have experienced particularly intense fire seasons recently, stretching global firefighting resources and making early detection of fires more crucial. Fires left unchecked can create their own weather systems, raining embers down on nearby communities and pushing dangerous smoke into big cities and even to neighbouring countries.
The sensor that picked up the October fire near the timber plantation is one of more than 40 in a network that covers an area nearly double the size of New York City in Australia’s Victoria state. The solar-powered, cylinder-shaped sensors are packed with instruments including optical and thermal cameras, flame detectors, particle counters for air quality, and ground-vibration readers. The data are publicly available online in real-time.
Attentis Pty. Ltd., a Melbourne, Australia-based company, finished installing the network in early 2019 as a pilot project to demonstrate the technology. They didn’t get much use detecting fires last season because the blazes were too far away.
Cameron McKenna, managing director at the company, said he is in talks to install similar sensor networks to improve fire detection capabilities in other parts of the country. “We use multiple methods of detection as opposed to a single method,” he said.
In Canberra, Australia’s capital, researchers are working with a local firefighting agency to install video cameras on four fire towers. One tower will also have a thermal camera installed.
A computer program will scan images from the thermal camera to detect fires, and a person will monitor the other video feeds. Researchers plan to develop a computer program to automatically scan the video feeds too, said Marta Yebra, director of the Australian National University Bushfire Initiative. The idea is to determine whether the cameras detect new fires quicker than the old-fashioned way of having a person in the fire tower, she said.
Rohan Scott, who leads the rural fire service in the Canberra area, said people in towers on average spot fires within seven minutes of ignition. But the towers are only staffed on high-risk days.
“If we can get detection 24/7 but at the same speed as a human-manned fire tower, then I think that would be a good outcome,” he said. “Anything quicker than that would be a definite bonus.”
Other ideas involve using drones fitted with lasers to create detailed maps so authorities know which areas have more dry vegetation and present a higher fire risk. One recent effort involved researchers poring over satellite imagery in an attempt to build a model for detecting extreme fire behaviour from orbit. They found that changes in smoke colours could help predict fire behaviour, according to the Minderoo Foundation, a nonprofit that sponsored the research.
One company, Fireball.International Pty. Ltd., says its machine-learning method can detect smoke from ground-based cameras and fires from heat signatures on thermal-satellite images. Its system is already being used by a big power company in California, and Fireball plans to begin pilot programs in Australia this fire season, executives said.
A prototype of the system detected smoke from fires within 15 minutes of ignition, while averaging less than one false positive a day per camera, according to an analysis published in a peer-reviewed journal in January. It said there is room for improvement.
“People are beginning to realise, ‘Gosh, we really can detect a fire in the first five minutes,’” said Tim Ball, an academic and co-founder of the company who is also a former firefighter.
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AI-powered travel agents are challenging online booking platforms, but Airbnb’s exclusive listings and direct customer relationships could give it an edge. Its next challenge: turning repeat guests into loyal customers through stronger rewards and innovation.
3 min
AI agents like Muse can already find you the cheapest hotel room in Paris. But they can’t locate a cheaper version of that Catskills cabin you’ve been eyeing, because it might only be available on Airbnb ABNB -1.43%decrease.
Investors should pay attention to that distinction. When Meta Platforms META 3.38%increase launched its Muse agent last month, travel stocks including Booking Holdings BKNG -1.19%decrease, Expedia and Airbnb all fell sharply.
Airbnb, though, has a real chance to emerge as a winner from the upheaval. The catch: Its stock already reflects above-industry growth. To justify its valuation, Airbnb will need to turn repeat customers into loyal ones.
Online travel companies make much of their money by simplifying search and collecting fees on bookings. AI agents threaten that model.
But not all travel marketplaces are equally exposed. Booking and Expedia largely sell hotel rooms that travelers can also book directly or through competing sites. If an agent can find the same room elsewhere, it has little reason to favor either Booking or Expedia.
Booking and Expedia also sell sponsored listings that steer travelers toward particular properties. Wells Fargo estimates those will account for about 8% of Expedia’s revenue and a much higher percentage of profit next year.
Airbnb has more ways to adapt. More than half its listings are available only on its platform, and more than 90% of its traffic comes directly to its app or website.
The customer relationship doesn’t end after booking, either. Bernstein analyst Richard Clarke notes that 90% of Airbnb guests go on to message their hosts about arrival times, keys or local taxes, giving Airbnb a connection to both sides of the transaction.
How the companies have responded to the possible agentic future is telling.
Expedia quickly signed on as a Muse partner. Airbnb hasn’t, and Chief Executive Brian Chesky says AI chatbots have so far been a boon, sending customers to Airbnb’s website.
Chatbots might work for buying paper towels, he quipped at a Skift conference last month, but people enjoy planning trips. He argued that browsing dozens of homes isn’t something a text-based chatbot is well suited for.
Still, the AI jitters arrived just as Airbnb was persuading investors it deserved its pricey multiple. For years after its 2020 initial public offering, the stock—which trades around 25 to 30 times forward earnings—went nowhere. Investors saw a mature operator trading at a growth premium.
This year, Airbnb growth seems to be accelerating. The stock jumped in August after the company raised its full-year revenue-growth guidance with its second-quarter results. Wells Fargo analyst Ken Gawrelski credits a run of product improvements under a revamped leadership team.
Among the changes: letting guests reserve now and pay later, adding hotels, and a redesigned home page and app. Investors were also betting on what comes next, including thousands more hotels, car rentals and, eventually, advertising.
The irony is that some of those growth drivers push Airbnb closer to the kind of business Booking and Expedia run—the kind of business AI agents could threaten. Even if AI agents capture a large share of travel planning and booking, Airbnb would capture the demand. But it and others would slowly lose control over how customers find and choose properties.
That would undermine the advertising opportunity investors are counting on. The bigger point for Airbnb and other marketplaces is that losing control of customer acquisition could mean surrendering some of their pricing power and margins.
To bulletproof itself, Airbnb might look to another e-commerce giant that has so far kept AI agents at arm’s length: Amazon.
Like Amazon, Airbnb has a loyal customer base and hard-to-match supply. But it has nothing like Prime membership to make customers think twice before taking their business elsewhere.
That is where loyalty comes in. Business travelers stick with Hilton or Marriott because enough nights earn them perks. Leisure travelers rarely accumulate stays with one chain. Airbnb has millions of listings and plenty of repeat customers, but no broad rewards program. It has begun experimenting with credits on hotel stays, but it should make loyalty a priority, not just a promotion.
Airbnb also plans to launch its own AI agent next year. But travelers using an agent might still want it to search Airbnb, Vrbo, Booking and hotel websites alike, rather than limit itself to one company’s inventory.
Eventually, Airbnb might have to strike deals with agent makers, using its unique homes as leverage to negotiate favorable terms, says Gawrelski. Such deals, along with rewards for loyal guests, could over the long term eat into the company’s roughly 13% take rate, he adds. But if they bring in enough additional bookings, the trade-off could be worthwhile.
History offers a reason not to panic. The shift from desktop to mobile unsettled investors in Alphabet and Amazon, but both emerged as major beneficiaries. Companies with valuable assets can come out of a platform shift stronger, if they adapt quickly enough.
Airbnb has homes the bots can’t easily replace. Now it needs to give guests a reason to insist on booking them there.
