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Cervest raises £3.7M for Earth Science AI platform to predict climate effects

Climate risk, including extreme events and the related pressures our environment, are fundamentally affecting the way businesses and governments operate — both tactically and strategically. Increasing climate volatility is causing food supply disruptions and increasing pressure on Enterprises (including financial institutions, insurers and producers) to disclose what’s going on.

The trouble is, while there is a lot of data about all this, its complexity, incompleteness and sheer volume is too vast for humans to process with the tools available today. So just as the climate changes, we are faced with “data chaos.” Equally, other parts of the world suffer from data scarcity, making it much harder to provide useful and timely analysis.

So the challenge is to address these issues simultaneously. So a new startup, Cervest, has created an AI-driven platform designed to inform the decision-making capabilities of businesses, governments and growers in the face of increasing climate volatility.

Cervest, has now closed a £3.7 million investment round to fund the launch of its real-time, climate forecasting platform.

The round was led by deep-tech investor Future Positive Capital, with co-investor Astanor Ventures . The seed-stage funding round brings the company’s total funding to more than £4.5 million.

Built on three years of research and development by a team of scientists, mathematicians, developers and engineers, Cervest says its Earth Science AI platform can analyze billions of data points to forecast how changes in the climate will impact the future of entire countries, right down to individual landscapes.

It does this by combining research and modeling techniques taken from proven Earth sciences — including atmospheric science, meteorology, hydrology and agronomy — with artificial intelligence, imaging, machine learning and Bayesian statistics.

Using large collections of satellite imagery and probability theory, the platform can identify signals, or early-warning signs, of extreme events such as floods, fires and strong winds. It also can spot changes in soil health and identify water risk.

Cervest says the platform could do such things as reveal the optimum location to build a new factory; warn a wheat grower that their crop yield isn’t expected to meet its targets; or be used by insurers to help them set premiums for the next 12 months.

The team comes from a network of more than 30 universities, including Imperial College, The Alan Turing Institute, Cambridge, UCL, Harvard and Oxford, and has published more than 60 peer-reviewed scientific papers.

A beta version of the platform is due to launch in Q1 2020.

Iggy Bassi, founder & CEO, Cervest said: “Our goal is to empower everyone to make informed decisions that improve the long-term resilience of our planet. Today decision-makers are struggling with climate uncertainty and extreme events and how they are affecting their business operations, assets, investments, or policy choices.”

Sofia Hmich, founder, Future Positive Capital said: “With reports suggesting we have fewer than 60 years of farming left unless drastic action is taken, the need for science-backed decisions could not be greater. Businesses and policymakers hold the key to change and with access to Cervest’s proprietary AI technology they can start to make that change a reality at low cost — before it’s too late.”

Bassi previously ran the impact-led agribusiness GADCO, which was supported by Acumen Fund, Soros, Gates Foundation, World Bank and Syngenta . Its impact was featured in UNDP, World Economic Forum, FT, The Guardian and Huff Post. He previously built a software company focused on data analytics.

Cervest was inspired by Bassi’s experience building a farm-to-market agribusiness whilst confronting first-hand the impacts of climate and natural resource volatilities.

The Cervest team includes eight scientists and four PhDs. Between them, they have published more than 60 peer-reviewed scientific papers with more than 3,000 citations in high-profile titles, including Nature, Proceedings of the National Academy of Sciences and The Royal Statistical Society.

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WorldCover raises $6M round for emerging markets’ climate insurance

WorldCover, a New York and Africa-based climate insurance provider to smallholder farmers, has raised a $6 million Series A round led by MS&AD Ventures.

Y Combinator, Western Technology Investment and EchoVC also participated in the round.

WorldCover’s platform uses satellite imagery, on-ground sensors, mobile phones and data analytics to create insurance options for farmers whose crop yields are affected adversely by weather events — primarily lack of rain.

The startup currently operates in Ghana, Uganda and Kenya . With the new funding, WorldCover aims to expand its insurance offerings to more emerging market countries.

“We’re looking at India, Mexico, Brazil, Indonesia. India could be first on an 18-month timeline for a launch,” WorldCover co-founder and chief executive Chris Sheehan said in an interview.

The company has served more than 30,000 farmers across its Africa operations. Smallholder farmers are those earning all or nearly all of their income from agriculture, farming on 10-20 acres of land and earning around $500 to $5,000, according to Sheehan.

Farmers connect to WorldCover by creating an account on its USSD mobile app. From there they can input their region and crop type and determine how much insurance they would like to buy and use mobile money to purchase a plan. WorldCover works with payments providers such as M-Pesa in Kenya and MTN Mobile Money in Ghana.

The service works on a sliding scale, where a customer can receive anywhere from 5x to 15x the amount of premium they have paid. If there is an adverse weather event, namely lack of rain, the farmer can file a claim via mobile phone. WorldCover then uses its data-analytics metrics to assess it, and, if approved, the farmer will receive an insurance payment via mobile money.

Common crops farmed by WorldCover clients include maize, rice and peanuts. It looks to add coffee, cocoa and cashews to its coverage list.

For the moment, WorldCover only insures for events such as rainfall risk, but in the future it will look to include other weather events, such as tropical storms, in its insurance programs and platform data analytics.

The startup’s founder clarified that WorldCover’s model does not assess or provide insurance payouts specifically for climate change, though it does directly connect to the company’s business.

“We insure for adverse weather events that we believe climate change factors are exacerbating,” Sheehan explained. WorldCover also resells the risk of its policyholders to global reinsurers, such as Swiss Re and Nephila.

On the potential market size for WorldCover’s business, he highlights a 2018 Lloyd’s study that identified $163 billion of assets at risk, including agriculture, in emerging markets from negative, climate change-related events.

“That’s what WorldCover wants to go after…These are the kind of micro-systemic risks we think we can model and then create a micro product for a smallholder farmer that they can understand and will give them protection,” he said.

With the round, the startup will look to possibilities to update its platform to offer farming advice to smallholder farmers, in addition to insurance coverage.

WorldCover investor and EchoVC founder Eghosa Omoigui believes the startup’s insurance offerings can actually help farmers improve yield. “Weather-risk drives a lot of decisions with these farmers on what to plant, when to plant, and how much to plant,” he said. “With the crop insurance option, the farmer says, ‘Instead of one hector, I can now plant two or three, because I’m covered.’ ”

Insurance technology is another sector in Africa’s tech landscape filling up with venture-backed startups. Other insurance startups focusing on agriculture include Accion Venture Lab-backed Pula and South Africa based Mobbisurance.

With its new round and plans for global expansion, WorldCover joins a growing list of startups that have developed business models in Africa before raising rounds toward entering new markets abroad.

In 2018, Nigerian payment startup Paga announced plans to move into Asia and Latin America after raising $10 million. In 2019, South African tech-transit startup FlexClub partnered with Uber Mexico after a seed raise. And Lagos-based fintech startup TeamAPT announced in Q1 it was looking to expand globally after a $5 million Series A round.

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Medal.tv’s clipping service allows gamers to share the moments of their digital lives

As online gaming becomes the new social forum for living out virtual lives, a new startup called Medal.tv has raised $3.5 million for its in-game clipping service to capture and share the Kodak moments and digital memories that are increasingly happening in places like Fortnite or Apex Legends.

Digital worlds like Fortnite are now far more than just a massively multiplayer gaming space. They’re places where communities form, where social conversations happen and where, increasingly, people are spending the bulk of their time online. They even host concerts — like the one from EDM artist Marshmello, which drew (according to the DJ himself) roughly 10 million players onto the platform.

While several services exist to provide clips of live streams from gamers who broadcast on platforms like Twitch, Medal.tv bills itself as the first to offer clipping services for the private games that more casual gamers play among friends and far-flung strangers around the world.

“Essentially the next generation is spending the same time inside games that we used to playing sports outside and things like that,” says Medal.tv’s co-founder and chief executive, Pim DeWitte. “It’s not possible to tell how far it will go. People will capture as many if not more moments for the reason that it’s simpler.”

The company marks a return to the world of gaming for DeWitte, a serial entrepreneur who first started coding when he was 13 years old.

Hailing from a small town in the Netherlands called Nijmegen, DeWitte first reaped the rewards of startup success with a gaming company called SoulSplit. Built on the back of his popular YouTube channel, the SoulSplit game was launched with DeWitte’s childhood friend, Iggy Harmsen, and a fellow online gamer, Josh Lipson, who came on board as SoulSplit’s chief technology officer.

At its height, SoulSplit was bringing in $1 million in revenue and employed roughly 30 people, according to interviews with DeWitte.

The company shut down in 2015 and the co-founders split up to pursue other projects. For DeWitte that meant a stint working with Doctors Without Borders on an app called MapSwipe that would use satellite imagery to better locate people in the event of a humanitarian crisis. He also helped the nonprofit develop a tablet that could be used by doctors deployed to treat Ebola outbreaks.

Then in 2017, as social gaming was becoming more popular on games like Fortnite, DeWitte and his co-founders returned to the industry to launch Medal.tv.

It initially started as a marketing tool to get people interested in playing the games that DeWitte and his co-founders were hoping to develop. But as the clipping service took off, DeWitte and co. realized they potentially had a more interesting social service on their hands.

“We were going to build a mobile app and were going to load a bunch of videos of people playing games and then we we’re going to load videos of our games,” DeWitte says. 

The service allows users to capture the last 15 seconds of gameplay using different recording mechanisms based on game type. Medal.tv captures gameplay on a device and users can opt-in to record sound as well.

It is programmed so that it only records the game,” DeWitte says. “There is no inbound connection. It only calls for the API [and] all of the things that would be somewhat dangerous from a privacy perspective are all opt-in.”

There are roughly 30,000 users on the platform every week and around 15,000 daily active users, according to DeWitte. Launched last May, the company has been growing between 5 percent and 10 percent weekly, according to DeWitte. Typically, users are sharing clips through Discord, WhatsApp and Instagram direct messages, DeWitte said.

In addition to the consumer-facing clipping service, Medal also offers a data collection service that aggregates information about the clips that are shared by Medal’s users so game developers and streamers can get a sense of how clips are being shared across which platform.

“We look at clips as a form of communication and in most activity that we see, that’s how it’s being used,” says DeWitte.

But that information is also valuable to esports organizations to determine where they need to allocate new resources.

“Medal.tv Metrics is spectacular,” said Peter Levin, chairman of the Immortals esports organization, in a statement. “With it, any gaming organization gains clear, actionable insights into the organic reach of their content, and can build a roadmap to increase it in a measurable way.”

The activity that Medal was seeing was impressive enough to attract the attention of investors led by Backed VC and Initial Capital. Ridge Ventures, Makers Fund and Social Starts participated in the company’s $3.5 million round as well, with Alex Brunicki, a founding partner at Backed, and Matteo Vallone, principal at Initial, joining the company’s board.

“Emerging generations are experiencing moments inside games the same way we used to with sports and festivals growing up. Digital and physical identity are merging and the technology for gamers hasn’t evolved to support that,” said Brunicki in a statement.

Medal’s platform works with games like Apex Legends, Fortnite, Roblox, Minecraft and Oldschool Runescape (where DeWitte first cut his teeth in gaming).

“Friends are the main driver of game discovery, and game developers benefit from shareable games as a result. Medal.tv is trying to enable that without the complexity of streaming,” said Vallone, who previously headed up games for Google Play Europe, and now sits on the Medal board. 

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Agtech startup Imago AI is using computer vision to boost crop yields

Presenting onstage today in the 2018 TC Disrupt Berlin Battlefield is Indian agtech startup Imago AI, which is applying AI to help feed the world’s growing population by increasing crop yields and reducing food waste. As startup missions go, it’s an impressively ambitious one.

The team, which is based out of Gurgaon near New Delhi, is using computer vision and machine learning technology to fully automate the laborious task of measuring crop output and quality — speeding up what can be a very manual and time-consuming process to quantify plant traits, often involving tools like calipers and weighing scales, toward the goal of developing higher-yielding, more disease-resistant crop varieties.

Currently they say it can take seed companies between six and eight years to develop a new seed variety. So anything that increases efficiency stands to be a major boon.

And they claim their technology can reduce the time it takes to measure crop traits by up to 75 percent.

In the case of one pilot, they say a client had previously been taking two days to manually measure the grades of their crops using traditional methods like scales. “Now using this image-based AI system they’re able to do it in just 30 to 40 minutes,” says co-founder Abhishek Goyal.

Using AI-based image processing technology, they can also crucially capture more data points than the human eye can (or easily can), because their algorithms can measure and asses finer-grained phenotypic differences than a person might pick up on or be easily able to quantify just judging by eye alone.

“Some of the phenotypic traits they are not possible to identify manually,” says co-founder Shweta Gupta. “Maybe very tedious or for whatever all these laborious reasons. So now with this AI-enabled [process] we are now able to capture more phenotypic traits.

“So more coverage of phenotypic traits… and with this more coverage we are having more scope to select the next cycle of this seed. So this further improves the seed quality in the longer run.”

The wordy phrase they use to describe what their technology delivers is: “High throughput precision phenotyping.”

Or, put another way, they’re using AI to data-mine the quality parameters of crops.

“These quality parameters are very critical to these seed companies,” says Gupta. “Plant breeding is a very costly and very complex process… in terms of human resource and time these seed companies need to deploy.

“The research [on the kind of rice you are eating now] has been done in the previous seven to eight years. It’s a complete cycle… chain of continuous development to finally come up with a variety which is appropriate to launch in the market.”

But there’s more. The overarching vision is not only that AI will help seed companies make key decisions to select for higher-quality seed that can deliver higher-yielding crops, while also speeding up that (slow) process. Ultimately their hope is that the data generated by applying AI to automate phenotypic measurements of crops will also be able to yield highly valuable predictive insights.

Here, if they can establish a correlation between geotagged phenotypic measurements and the plants’ genotypic data (data which the seed giants they’re targeting would already hold), the AI-enabled data-capture method could also steer farmers toward the best crop variety to use in a particular location and climate condition — purely based on insights triangulated and unlocked from the data they’re capturing.

One current approach in agriculture to selecting the best crop for a particular location/environment can involve using genetic engineering. Though the technology has attracted major controversy when applied to foodstuffs.

Imago AI hopes to arrive at a similar outcome via an entirely different technology route, based on data and seed selection. And, well, AI’s uniform eye informing key agriculture decisions.

“Once we are able to establish this sort of relation this is very helpful for these companies and this can further reduce their total seed production time from six to eight years to very less number of years,” says Goyal. “So this sort of correlation we are trying to establish. But for that initially we need to complete very accurate phenotypic data.”

“Once we have enough data we will establish the correlation between phenotypic data and genotypic data and what will happen after establishing this correlation we’ll be able to predict for these companies that, with your genomics data, and with the environmental conditions, and we’ll predict phenotypic data for you,” adds Gupta.

“That will be highly, highly valuable to them because this will help them in reducing their time resources in terms of this breeding and phenotyping process.”

“Maybe then they won’t really have to actually do a field trial,” suggests Goyal. “For some of the traits they don’t really need to do a field trial and then check what is going to be that particular trait if we are able to predict with a very high accuracy if this is the genomics and this is the environment, then this is going to be the phenotype.”

So — in plainer language — the technology could suggest the best seed variety for a particular place and climate, based on a finer-grained understanding of the underlying traits.

In the case of disease-resistant plant strains it could potentially even help reduce the amount of pesticides farmers use, say, if the the selected crops are naturally more resilient to disease.

While, on the seed generation front, Gupta suggests their approach could shrink the production time frame — from up to eight years to “maybe three or four.”

“That’s the amount of time-saving we are talking about,” she adds, emphasizing the really big promise of AI-enabled phenotyping is a higher amount of food production in significantly less time.

As well as measuring crop traits, they’re also using computer vision and machine learning algorithms to identify crop diseases and measure with greater precision how extensively a particular plant has been affected.

This is another key data point if your goal is to help select for phenotypic traits associated with better natural resistance to disease, with the founders noting that around 40 percent of the world’s crop load is lost (and so wasted) as a result of disease.

And, again, measuring how diseased a plant is can be a judgement call for the human eye — resulting in data of varying accuracy. So by automating disease capture using AI-based image analysis the recorded data becomes more uniformly consistent, thereby allowing for better quality benchmarking to feed into seed selection decisions, boosting the entire hybrid production cycle.

Sample image processed by Imago AI showing the proportion of a crop affected by disease

In terms of where they are now, the bootstrapping, nearly year-old startup is working off data from a number of trials with seed companies — including a recurring paying client they can name (DuPont Pioneer); and several paid trials with other seed firms they can’t (because they remain under NDA).

Trials have taken place in India and the U.S. so far, they tell TechCrunch.

“We don’t really need to pilot our tech everywhere. And these are global [seed] companies, present in 30, 40 countries,” adds Goyal, arguing their approach naturally scales. “They test our technology at a single country and then it’s very easy to implement it at other locations.”

Their imaging software does not depend on any proprietary camera hardware. Data can be captured with tablets or smartphones, or even from a camera on a drone or using satellite imagery, depending on the sought for application.

Although for measuring crop traits like length they do need some reference point to be associated with the image.

“That can be achieved by either fixing the distance of object from the camera or by placing a reference object in the image. We use both the methods, as per convenience of the user,” they note on that.

While some current phenotyping methods are very manual, there are also other image-processing applications in the market targeting the agriculture sector.

But Imago AI’s founders argue these rival software products are only partially automated — “so a lot of manual input is required,” whereas they couch their approach as fully automated, with just one initial manual step of selecting the crop to be quantified by their AI’s eye.

Another advantage they flag up versus other players is that their approach is entirely non-destructive. This means crop samples do not need to be plucked and taken away to be photographed in a lab, for example. Rather, pictures of crops can be snapped in situ in the field, with measurements and assessments still — they claim — accurately extracted by algorithms which intelligently filter out background noise.

“In the pilots that we have done with companies, they compared our results with the manual measuring results and we have achieved more than 99 percent accuracy,” is Goyal’s claim.

While, for quantifying disease spread, he points out it’s just not manually possible to make exact measurements. “In manual measurement, an expert is only able to provide a certain percentage range of disease severity for an image example; (25-40 percent) but using our software they can accurately pin point the exact percentage (e.g. 32.23 percent),” he adds.

They are also providing additional support for seed researchers — by offering a range of mathematical tools with their software to support analysis of the phenotypic data, with results that can be easily exported as an Excel file.

“Initially we also didn’t have this much knowledge about phenotyping, so we interviewed around 50 researchers from technical universities, from these seed input companies and interacted with farmers — then we understood what exactly is the pain-point and from there these use cases came up,” they add, noting that they used WhatsApp groups to gather intel from local farmers.

While seed companies are the initial target customers, they see applications for their visual approach for optimizing quality assessment in the food industry too — saying they are looking into using computer vision and hyper-spectral imaging data to do things like identify foreign material or adulteration in production line foodstuffs.

“Because in food companies a lot of food is wasted on their production lines,” explains Gupta. “So that is where we see our technology really helps — reducing that sort of wastage.”

“Basically any visual parameter which needs to be measured that can be done through our technology,” adds Goyal.

They plan to explore potential applications in the food industry over the next 12 months, while focusing on building out their trials and implementations with seed giants. Their target is to have between 40 to 50 companies using their AI system globally within a year’s time, they add.

While the business is revenue-generating now — and “fully self-enabled” as they put it — they are also looking to take in some strategic investment.

“Right now we are in touch with a few investors,” confirms Goyal. “We are looking for strategic investors who have access to agriculture industry or maybe food industry… but at present haven’t raised any amount.”


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Apple is rebuilding Maps from the ground up

I’m not sure if you’re aware, but the launch of Apple Maps went poorly. After a rough first impression, an apology from the CEO, several years of patching holes with data partnerships and some glimmers of light with long-awaited transit directions and improvements in business, parking and place data, Apple Maps is still not where it needs to be to be considered a world-class service.

Maps needs fixing.

Apple, it turns out, is aware of this, so it’s re-building the maps part of Maps.

It’s doing this by using first-party data gathered by iPhones with a privacy-first methodology and its own fleet of cars packed with sensors and cameras. The new product will launch in San Francisco and the Bay Area with the next iOS 12 beta and will cover Northern California by fall.

Every version of iOS will get the updated maps eventually, and they will be more responsive to changes in roadways and construction, more visually rich depending on the specific context they’re viewed in and feature more detailed ground cover, foliage, pools, pedestrian pathways and more.

This is nothing less than a full re-set of Maps and it’s been four years in the making, which is when Apple began to develop its new data-gathering systems. Eventually, Apple will no longer rely on third-party data to provide the basis for its maps, which has been one of its major pitfalls from the beginning.

“Since we introduced this six years ago — we won’t rehash all the issues we’ve had when we introduced it — we’ve done a huge investment in getting the map up to par,” says Apple SVP Eddy Cue, who now owns Maps, in an interview last week. “When we launched, a lot of it was all about directions and getting to a certain place. Finding the place and getting directions to that place. We’ve done a huge investment of making millions of changes, adding millions of locations, updating the map and changing the map more frequently. All of those things over the past six years.”

But, Cue says, Apple has room to improve on the quality of Maps, something that most users would agree on, even with recent advancements.

“We wanted to take this to the next level,” says Cue. “We have been working on trying to create what we hope is going to be the best map app in the world, taking it to the next step. That is building all of our own map data from the ground up.”

In addition to Cue, I spoke to Apple VP Patrice Gautier and more than a dozen Apple Maps team members at its mapping headquarters in California this week about its efforts to re-build Maps, and to do it in a way that aligned with Apple’s very public stance on user privacy.

If, like me, you’re wondering whether Apple thought of building its own maps from scratch before it launched Maps, the answer is yes. At the time, there was a choice to be made about whether or not it wanted to be in the business of maps at all. Given that the future of mobile devices was becoming very clear, it knew that mapping would be at the core of nearly every aspect of its devices, from photos to directions to location services provided to apps. Decision made, Apple plowed ahead, building a product that relied on a patchwork of data from partners like TomTom, OpenStreetMap and other geo data brokers. The result was underwhelming.

Almost immediately after Apple launched Maps, it realized that it was going to need help and it signed on a bunch of additional data providers to fill the gaps in location, base map, point-of-interest and business data.

It wasn’t enough.

“We decided to do this just over four years ago. We said, ‘Where do we want to take Maps? What are the things that we want to do in Maps?’ We realized that, given what we wanted to do and where we wanted to take it, we needed to do this ourselves,” says Cue.

Because Maps are so core to so many functions, success wasn’t tied to just one function. Maps needed to be great at transit, driving and walking — but also as a utility used by apps for location services and other functions.

Cue says that Apple needed to own all of the data that goes into making a map, and to control it from a quality as well as a privacy perspective.

There’s also the matter of corrections, updates and changes entering a long loop of submission to validation to update when you’re dealing with external partners. The Maps team would have to be able to correct roads, pathways and other updating features in days or less, not months. Not to mention the potential competitive advantages it could gain from building and updating traffic data from hundreds of millions of iPhones, rather than relying on partner data.

Cue points to the proliferation of devices running iOS, now over a billion, as a deciding factor to shift its process.

“We felt like because the shift to devices had happened — building a map today in the way that we were traditionally doing it, the way that it was being done — we could improve things significantly, and improve them in different ways,” he says. “One is more accuracy. Two is being able to update the map faster based on the data and the things that we’re seeing, as opposed to driving again or getting the information where the customer’s proactively telling us. What if we could actually see it before all of those things?”

I query him on the rapidity of Maps updates, and whether this new map philosophy means faster changes for users.

“The truth is that Maps needs to be [updated more], and even are today,” says Cue. “We’ll be doing this even more with our new maps, [with] the ability to change the map in real time and often. We do that every day today. This is expanding us to allow us to do it across everything in the map. Today, there’s certain things that take longer to change.

“For example, a road network is something that takes a much longer time to change currently. In the new map infrastructure, we can change that relatively quickly. If a new road opens up, immediately we can see that and make that change very, very quickly around it. It’s much, much more rapid to do changes in the new map environment.”

So a new effort was created to begin generating its own base maps, the very lowest building block of any really good mapping system. After that, Apple would begin layering on living location data, high-resolution satellite imagery and brand new intensely high-resolution image data gathered from its ground cars until it had what it felt was a “best in class” mapping product.

There is only really one big company on earth that owns an entire map stack from the ground up: Google .

Apple knew it needed to be the other one. Enter the vans.

Apple vans spotted

Though the overall project started earlier, the first glimpse most folks had of Apple’s renewed efforts to build the best Maps product was the vans that started appearing on the roads in 2015 with “Apple Maps” signs on the side. Capped with sensors and cameras, these vans popped up in various cities and sparked rampant discussion and speculation.

The new Apple Maps will be the first time the data collected by these vans is actually used to construct and inform its maps. This is their coming out party.

Some people have commented that Apple’s rigs look more robust than the simple GPS + Camera arrangements on other mapping vehicles — going so far as to say they look more along the lines of something that could be used in autonomous vehicle training.

Apple isn’t commenting on autonomous vehicles, but there’s a reason the arrays look more advanced: they are.

Earlier this week I took a ride in one of the vans as it ran a sample route to gather the kind of data that would go into building the new maps. Here’s what’s inside.

In addition to a beefed-up GPS rig on the roof, four LiDAR arrays mounted at the corners and eight cameras shooting overlapping high-resolution images, there’s also the standard physical measuring tool attached to a rear wheel that allows for precise tracking of distance and image capture. In the rear there is a surprising lack of bulky equipment. Instead, it’s a straightforward Mac Pro bolted to the floor, attached to an array of solid state drives for storage. A single USB cable routes up to the dashboard where the actual mapping-capture software runs on an iPad.

While mapping, a driver…drives, while an operator takes care of the route, ensuring that a coverage area that has been assigned is fully driven, as well as monitoring image capture. Each drive captures thousands of images as well as a full point cloud (a 3D map of space defined by dots that represent surfaces) and GPS data. I later got to view the raw data presented in 3D and it absolutely looks like the quality of data you would need to begin training autonomous vehicles.

More on why Apple needs this level of data detail later.

When the images and data are captured, they are then encrypted on the fly and recorded on to the SSDs. Once full, the SSDs are pulled out, replaced and packed into a case, which is delivered to Apple’s data center, where a suite of software eliminates from the images private information like faces, license plates and other info. From the moment of capture to the moment they’re sanitized, they are encrypted with one key in the van and the other key in the data center. Technicians and software that are part of its mapping efforts down the pipeline from there never see unsanitized data.

This is just one element of Apple’s focus on the privacy of the data it is utilizing in New Maps.

Probe data and privacy

Throughout every conversation I have with any member of the team throughout the day, privacy is brought up, emphasized. This is obviously by design, as Apple wants to impress upon me as a journalist that it’s taking this very seriously indeed, but it doesn’t change the fact that it’s evidently built in from the ground up and I could not find a false note in any of the technical claims or the conversations I had.

Indeed, from the data security folks to the people whose job it is to actually make the maps work well, the constant refrain is that Apple does not feel that it is being held back in any way by not hoovering every piece of customer-rich data it can, storing and parsing it.

The consistent message is that the team feels it can deliver a high-quality navigation, location and mapping product without the directly personal data used by other platforms.

“We specifically don’t collect data, even from point A to point B,” notes Cue. “We collect data — when we do it — in an anonymous fashion, in subsections of the whole, so we couldn’t even say that there is a person that went from point A to point B. We’re collecting the segments of it. As you can imagine, that’s always been a key part of doing this. Honestly, we don’t think it buys us anything [to collect more]. We’re not losing any features or capabilities by doing this.”

The segments that he is referring to are sliced out of any given person’s navigation session. Neither the beginning or the end of any trip is ever transmitted to Apple. Rotating identifiers, not personal information, are assigned to any data or requests sent to Apple and it augments the “ground truth” data provided by its own mapping vehicles with this “probe data” sent back from iPhones.

Because only random segments of any person’s drive is ever sent and that data is completely anonymized, there is never a way to tell if any trip was ever a single individual. The local system signs the IDs and only it knows to whom that ID refers. Apple is working very hard here to not know anything about its users. This kind of privacy can’t be added on at the end, it has to be woven in at the ground level.

Because Apple’s business model does not rely on it serving to you, say, an ad for a Chevron on your route, it doesn’t need to even tie advertising identifiers to users.

Any personalization or Siri requests are all handled on-board by the iOS device’s processor. So if you get a drive notification that tells you it’s time to leave for your commute, that’s learned, remembered and delivered locally, not from Apple’s servers.

That’s not new, but it’s important to note given the new thing to take away here: Apple is flipping on the power of having millions of iPhones passively and actively improving their mapping data in real time.

In short: Traffic, real-time road conditions, road systems, new construction and changes in pedestrian walkways are about to get a lot better in Apple Maps.

The secret sauce here is what Apple calls probe data. Essentially little slices of vector data that represent direction and speed transmitted back to Apple completely anonymized with no way to tie it to a specific user or even any given trip. It’s reaching in and sipping a tiny amount of data from millions of users instead, giving it a holistic, real-time picture without compromising user privacy.

If you’re driving, walking or cycling, your iPhone can already tell this. Now if it knows you’re driving, it also can send relevant traffic and routing data in these anonymous slivers to improve the entire service. This only happens if your Maps app has been active, say you check the map, look for directions, etc. If you’re actively using your GPS for walking or driving, then the updates are more precise and can help with walking improvements like charting new pedestrian paths through parks — building out the map’s overall quality.

All of this, of course, is governed by whether you opted into location services, and can be toggled off using the maps location toggle in the Privacy section of settings.

Apple says that this will have a near zero effect on battery life or data usage, because you’re already using the ‘maps’ features when any probe data is shared and it’s a fraction of what power is being drawn by those activities.

From the point cloud on up

But maps cannot live on ground truth and mobile data alone. Apple is also gathering new high-resolution satellite data to combine with its ground truth data for a solid base map. It’s then layering satellite imagery on top of that to better determine foliage, pathways, sports facilities, building shapes and pathways.

After the downstream data has been cleaned up of license plates and faces, it gets run through a bunch of computer vision programming to pull out addresses, street signs and other points of interest. These are cross referenced to publicly available data like addresses held by the city and new construction of neighborhoods or roadways that comes from city planning departments.

But one of the special sauce bits that Apple is adding to the mix of mapping tools is a full-on point cloud that maps in 3D the world around the mapping van. This allows them all kinds of opportunities to better understand what items are street signs (retro-reflective rectangular object about 15 feet off the ground? Probably a street sign) or stop signs or speed limit signs.

It seems like it also could enable positioning of navigation arrows in 3D space for AR navigation, but Apple declined to comment on “any future plans” for such things.

Apple also uses semantic segmentation and Deep Lambertian Networks to analyze the point cloud coupled with the image data captured by the car and from high-resolution satellites in sync. This allows 3D identification of objects, signs, lanes of traffic and buildings and separation into categories that can be highlighted for easy discovery.

The coupling of high-resolution image data from car and satellite, plus a 3D point cloud, results in Apple now being able to produce full orthogonal reconstructions of city streets with textures in place. This is massively higher-resolution and easier to see, visually. And it’s synchronized with the “panoramic” images from the car, the satellite view and the raw data. These techniques are used in self-driving applications because they provide a really holistic view of what’s going on around the car. But the ortho view can do even more for human viewers of the data by allowing them to “see” through brush or tree cover that would normally obscure roads, buildings and addresses.

This is hugely important when it comes to the next step in Apple’s battle for supremely accurate and useful Maps: human editors.

Apple has had a team of tool builders working specifically on a toolkit that can be used by human editors to vet and parse data, street by street. The editor’s suite includes tools that allow human editors to assign specific geometries to flyover buildings (think Salesforce tower’s unique ridged dome) that allow them to be instantly recognizable. It lets editors look at real images of street signs shot by the car right next to 3D reconstructions of the scene and computer vision detection of the same signs, instantly recognizing them as accurate or not.

Another tool corrects addresses, letting an editor quickly move an address to the center of a building, determine whether they’re misplaced and shift them around. It also allows for access points to be set, making Apple Maps smarter about the “last 50 feet” of your journey. You’ve made it to the building, but what street is the entrance actually on? And how do you get into the driveway? With a couple of clicks, an editor can make that permanently visible.

“When we take you to a business and that business exists, we think the precision of where we’re taking you to, from being in the right building,” says Cue. “When you look at places like San Francisco or big cities from that standpoint, you have addresses where the address name is a certain street, but really, the entrance in the building is on another street. They’ve done that because they want the better street name. Those are the kinds of things that our new Maps really is going to shine on. We’re going to make sure that we’re taking you to exactly the right place, not a place that might be really close by.”

Water, swimming pools (new to Maps entirely), sporting areas and vegetation are now more prominent and fleshed out thanks to new computer vision and satellite imagery applications. So Apple had to build editing tools for those, as well.

Many hundreds of editors will be using these tools, in addition to the thousands of employees Apple already has working on maps, but the tools had to be built first, now that Apple is no longer relying on third parties to vet and correct issues.

And the team also had to build computer vision and machine learning tools that allow it to determine whether there are issues to be found at all.

Anonymous probe data from iPhones, visualized, looks like thousands of dots, ebbing and flowing across a web of streets and walkways, like a luminescent web of color. At first, chaos. Then, patterns emerge. A street opens for business, and nearby vessels pump orange blood into the new artery. A flag is triggered and an editor looks to see if a new road needs a name assigned.

A new intersection is added to the web and an editor is flagged to make sure that the left turn lanes connect correctly across the overlapping layers of directional traffic. This has the added benefit of massively improved lane guidance in the new Apple Maps.

Apple is counting on this combination of human and AI flagging to allow editors to first craft base maps and then also maintain them as the ever-changing biomass wreaks havoc on roadways, addresses and the occasional park.

Here there be Helvetica

Apple’s new Maps, like many other digital maps, display vastly differently depending on scale. If you’re zoomed out, you get less detail. If you zoom in, you get more. But Apple has a team of cartographers on staff that work on more cultural, regional and artistic levels to ensure that its Maps are readable, recognizable and useful.

These teams have goals that are at once concrete and a bit out there — in the best traditions of Apple pursuits that intersect the technical with the artistic.

The maps need to be usable, but they also need to fulfill cognitive goals on cultural levels that go beyond what any given user might know they need. For instance, in the U.S., it is very common to have maps that have a relatively low level of detail even at a medium zoom. In Japan, however, the maps are absolutely packed with details at the same zoom, because that increased information density is what is expected by users.

This is the department of details. They’ve reconstructed replicas of hundreds of actual road signs to make sure that the shield on your navigation screen matches the one you’re seeing on the highway road sign. When it comes to public transport, Apple licensed all of the type faces that you see on your favorite subway systems, like Helvetica for NYC. And the line numbers are in the exact same order that you’re going to see them on the platform signs.

It’s all about reducing the cognitive load that it takes to translate the physical world you have to navigate into the digital world represented by Maps.

Bottom line

The new version of Apple Maps will be in preview next week with just the Bay Area of California going live. It will be stitched seamlessly into the “current” version of Maps, but the difference in quality level should be immediately visible based on what I’ve seen so far.

Better road networks, more pedestrian information, sports areas like baseball diamonds and basketball courts, more land cover, including grass and trees, represented on the map, as well as buildings, building shapes and sizes that are more accurate. A map that feels more like the real world you’re actually traveling through.

Search is also being revamped to make sure that you get more relevant results (on the correct continents) than ever before. Navigation, especially pedestrian guidance, also gets a big boost. Parking areas and building details to get you the last few feet to your destination are included, as well.

What you won’t see, for now, is a full visual redesign.

“You’re not going to see huge design changes on the maps,” says Cue. “We don’t want to combine those two things at the same time because it would cause a lot of confusion.”

Apple Maps is getting the long-awaited attention it really deserves. By taking ownership of the project fully, Apple is committing itself to actually creating the map that users expected of it from the beginning. It’s been a lingering shadow on iPhones, especially, where alternatives like Google Maps have offered more robust feature sets that are so easy to compare against the native app but impossible to access at the deep system level.

The argument has been made ad nauseam, but it’s worth saying again that if Apple thinks that mapping is important enough to own, it should own it. And that’s what it’s trying to do now.

“We don’t think there’s anybody doing this level of work that we’re doing,” adds Cue. “We haven’t announced this. We haven’t told anybody about this. It’s one of those things that we’ve been able to keep pretty much a secret. Nobody really knows about it. We’re excited to get it out there. Over the next year, we’ll be rolling it out, section by section in the U.S.”

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Google Maps gets multi-stop directions and vacation memories on mobile

Camping site with a caravan and a four wheel drive parked under a tree by the Darling River in Australia. Google is bringing long awaited multi-stop directions to mobile with its new summer update. Travelers can now hit as many tourist traps as they want on their cross country treks. Just like in the web version, users can swiftly rearrange the order of stops. Android users will get the feature first, followed by iOS in the near future. The summer update caps off an active week for Maps.… Read More

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