Google Aerial Maps Street View – Google Street View has become an incredibly useful way to learn about the world without ever setting foot on it. People use it to plan trips, research vacation destinations, and keep track of friends and enemies.
But researchers have found even more secret uses. In 2017, a team of researchers used these images to study the distribution of car types in the United States, and then used that data to determine the country’s demographic composition. It turns out that the car you drive is a reliable proxy for your income level, education, occupation, and how you vote in elections.
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Now a second group has gone even further. Lukasz Kiedzinski of Stanford University in California and Kinga Kita-Wychoska of the University of Warsaw in Poland used street-view images of people’s homes to determine their likelihood of being involved in a car accident. This is valuable information that the insurance company can use to determine premiums.
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The results raise important questions about how personal information can leak from seemingly innocuous data sets and whether organizations should be able to use it for business purposes.
The researchers’ method is simple. They started with a dataset of 20,000 records of people who bought car insurance in Poland between 2013 and 2015.
Each record contained the address of the insured and the number of claims made by him in the period of 2013-15. The insurer also shared its predictions of future claims, calculated using its sophisticated risk model, which takes into account the policyholder’s zip code and the driver’s age, gender, claims history and more.
Kiedzki and Keita-Wojichowska investigated whether they could make more accurate predictions using Google Street View images of insured homes.
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To find out, the researchers entered each insurer’s address into Google Street View and downloaded an image of the residence. They classified housing according to its type (detached house, terrace house, flat block, etc.), age and condition. Finally, the researchers crunched this data set to see how policyholder coverage correlated with claim probability.
The results are amazing. It turns out that a policyholder’s place of residence is a surprisingly good predictor of their claim. like zip code,” say Kiedzki and Kita-Wojichowska.
When these factors are added to an insurer’s complex risk model, it improves its predictive power by 2 percent. To put this into perspective, the insurer’s model is only 8 percent better than the null model and is based on a large data set that includes variables such as age, gender, and claim history.
Therefore, Google Street View technology has the potential to significantly improve forecasting, and the current work is just a proof of principle. According to the researchers, accuracy could be improved by using larger data sets and better data analysis.
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The researcher’s perspective raises a number of important questions about how personal data is used. Polish policyholders may be surprised to learn that their home address has been entered into Google Street View to obtain and analyze an image of their residence.
An interesting question is whether they have given informed consent for this activity due to strict European data privacy laws, or can an insurance company use the data in this way? Kiedzki and Kita-Wojichowska say, “A company’s consent for a customer to store their address is not consent to store information about their home location.”
And this approach can open a Pandora’s box of data analysis. Because of the proven relationship between insurance risk models and credit risk scoring, Kiedzki and Keita-Wojchoska say the insurance industry may soon be followed by banks.
The ability to collect, analyze and exploit information has increased dramatically in recent years. This ability has outstripped most people’s understanding of what is possible with their data, and will certainly outstrip what laws can supposedly be put in place to control it. .
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However, Google is not the only company collecting street-level imagery. “Such practices raise privacy concerns about data stored in public Google Street View, Microsoft’s Bing Street Maps, Mapillary, or similar private datasets,” Kiedziski and Kita-Wozichowska say.
This type of work probably raises the question of whether these questions should be collected and stored. In Germany, where privacy is a hot topic of public debate, Google is banned from collecting street view images. Such a ban cannot be a last resort
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The provider’s philosophy, which focused on the long term, is a conservative project focused on decision-making among a small group of technocrats. When flying, I’m totally a window seat (and I can’t wait to start flying again…or at least get out of my apartment.) Not because the drink cart hits my shoulder (though which I have), or because I like blank stares. Infinite skies (which I do), but when we land I enjoy looking at the streets, buildings and skyline of my destination. Watching the cars driving by, the shadows of the skyscrapers on the streets or the reflection of the sun on the water is exciting. For most of human history, it was impossible to even imagine what the Earth looks like from above, and only in the last century have we been able to picture it.
Today, satellite images are one of the most popular features of Google Maps. Capturing the world from above is a difficult task, matching millions of images to precise locations. But how exactly do satellite images work? How often are the images updated? Something big enough to deliver satellite imagery to over 1 billion users? What is space?
To answer these questions, I reached out to satellite imaging technology expert Matt Manoulides. Matt is a Geospatial Data Strategist at Google. He has worked at Google for over 14 years and gave me an aerial view of how satellite imagery works.
The satellite and aerial photo mosaics you can see on Google Maps and Google Earth come from a variety of providers, including agencies, government agencies, geological survey organizations, and commercial imagery providers. These photos were taken on different dates and in different light and weather conditions
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In fact, an entire industry is devoted to aerial surveys. Companies drill holes in the bottom of the plane, and cameras take pictures as the plane flies overhead. In many parts of the world this happens all the time. world In the United States, where there is no established aerial survey market, we rely on satellites. Aerial surveys give us very high quality images that are clear enough to make accurate maps. Satellites produce lower quality images, but are still useful because they provide global coverage.
“Google receives commercially available satellite imagery from various third-party locations and our team stitches the images together,” Matt told me. It’s a process called photogrammetry, and according to Matt, we’ve been able to automate our photogrammetry process using machine learning to accurately place images and improve resolution.
For aerial data, images are delivered on hard drives and we upload them to Google Cloud. For satellite imagery, data is uploaded directly from our provider to Google Cloud. Images are delivered raw, meaning they have not been ground yet and are divided into red, blue, and green. Also, panchromatic images that contain finer images. the details. Then we combine the jumbo images so that they all line up and fit in the real world and look nice overall.
“Our goal is to update satellite images of the places where the most changes are occurring,” says Matt. For example, because big cities are always growing, we try to update our satellite images every year, for medium-sized cities every two years, and for smaller cities every three years. Let’s update this image once. In general, we aim to keep populated areas fresh regularly and keep up with the changing world when we think there is a lot of construction or road building going on.
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Matt points out that sometimes the way images are assembled can create optical illusions. A common example is the “wrecked ship” which
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