Tag Archives: Google Earth

Using machine learning to help people make smart decisions about solar energy

 A few years ago, when my family was first deciding whether or not to go solar, I remember driving around the neighborhood, looking at all the solar arrays on nearby rooftops. It made me realize: Wow, solar isn’t some futuristic concept, it’s already part of the fabric of my town! Seeing that others around me were already benefiting from solar helped me decide to do the same.

We want to make it easy for people to make informed decisions about whether to invest in solar. Project Sunroof already shows you solar potential and cost saving for more than 60 million individual homes. Today we’re adding a new feature, Project Sunroof Data Explorer, which shows a map of existing solar installations in neighborhoods throughout the United States. Now instead of driving street to street, it’s a little easier to see if houses around you and communities nearby have already gone solar.  
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Click on “existing arrays” in the upper right corner to see number of existing installations in your region

This feature combines machine learning with imagery from Google Maps and Google Earth to provide an estimate of how many houses in an area have solar. We started by taking in high-resolution imagery of rooftops and manually identifying solar installations. We then used that data as the initial training set for our algorithm. After many iterations, our machine learning algorithms can now automatically find and identify installations in the imagery (both photovoltaic panels, which produce electricity, and solar hot water heaters). Even for machines, practice makes perfect!

2

So far we’ve identified around 700,000 installations in the U.S. and over time, as we continue to train the algorithms and apply improvements, we will be able to find and show more installations. We hope that this new feature will provide policy makers, communities and individuals with more information to help make smarter decisions in their transition to cleaner power sources.

Using machine learning to help people make smart decisions about solar energy

 A few years ago, when my family was first deciding whether or not to go solar, I remember driving around the neighborhood, looking at all the solar arrays on nearby rooftops. It made me realize: Wow, solar isn’t some futuristic concept, it’s already part of the fabric of my town! Seeing that others around me were already benefiting from solar helped me decide to do the same.

We want to make it easy for people to make informed decisions about whether to invest in solar. Project Sunroof already shows you solar potential and cost saving for more than 60 million individual homes. Today we’re adding a new feature, Project Sunroof Data Explorer, which shows a map of existing solar installations in neighborhoods throughout the United States. Now instead of driving street to street, it’s a little easier to see if houses around you and communities nearby have already gone solar.  
one
Click on “existing arrays” in the upper right corner to see number of existing installations in your region

This feature combines machine learning with imagery from Google Maps and Google Earth to provide an estimate of how many houses in an area have solar. We started by taking in high-resolution imagery of rooftops and manually identifying solar installations. We then used that data as the initial training set for our algorithm. After many iterations, our machine learning algorithms can now automatically find and identify installations in the imagery (both photovoltaic panels, which produce electricity, and solar hot water heaters). Even for machines, practice makes perfect!

2

So far we’ve identified around 700,000 installations in the U.S. and over time, as we continue to train the algorithms and apply improvements, we will be able to find and show more installations. We hope that this new feature will provide policy makers, communities and individuals with more information to help make smarter decisions in their transition to cleaner power sources.

Using machine learning to help people make smart decisions about solar energy

 A few years ago, when my family was first deciding whether or not to go solar, I remember driving around the neighborhood, looking at all the solar arrays on nearby rooftops. It made me realize: Wow, solar isn’t some futuristic concept, it’s already part of the fabric of my town! Seeing that others around me were already benefiting from solar helped me decide to do the same.

We want to make it easy for people to make informed decisions about whether to invest in solar. Project Sunroof already shows you solar potential and cost saving for more than 60 million individual homes. Today we’re adding a new feature, Project Sunroof Data Explorer, which shows a map of existing solar installations in neighborhoods throughout the United States. Now instead of driving street to street, it’s a little easier to see if houses around you and communities nearby have already gone solar.
one
Click on “existing arrays” in the upper right corner to see number of existing installations in your region

This feature combines machine learning with imagery from Google Maps and Google Earth to provide an estimate of how many houses in an area have solar. We started by taking in high-resolution imagery of rooftops and manually identifying solar installations. We then used that data as the initial training set for our algorithm. After many iterations, our machine learning algorithms can now automatically find and identify installations in the imagery (both photovoltaic panels, which produce electricity, and solar hot water heaters). Even for machines, practice makes perfect!

2

So far we’ve identified around 700,000 installations in the U.S. and over time, as we continue to train the algorithms and apply improvements, we will be able to find and show more installations. We hope that this new feature will provide policy makers, communities and individuals with more information to help make smarter decisions in their transition to cleaner power sources.

Source: Google LatLong


Dive into World Oceans Day with Google Earth and Maps

This World Oceans Day, explore our oceans and learn from experts leading marine conservation efforts around the world with Street View and Google Earth.

We hope these stories and sights will inspire you to get involved and help protect our vast and fragile oceans. So jump on in, the water’s fine!Start your underwater adventure in Voyager. With the Ocean Agency, tour Raja Ampat, Indonesia’s rich marine center containing 76 percent of the world’s coral species and 36 percent of the world’s reef fish species—more than any other marine region in the world. Then head to the Gulf of California with Dr. Sylvia Earle to see leaping mobula rays and learn about efforts to combat shark finning. Finally, head south to swim alongside the playful sea lions of the Galápagos Islands, one of the most biodiverse and unique places on the planet.

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Once you’ve gotten your sea legs in Voyager, sail on over to Street View for even more beautiful blues. Go tete-a-tete with turtles in the Great Barrier Reef off Heron Island, Australia, snorkel through colorful coral in DA Bai Shah, Taiwan, and enroll yourself in a school of fish in Bali.

SV_Oceans_Heron Island.png
Heron Island, Great Barrier Reef
SV_Oceans_Da Bai Sha.png
Da Bai Sha, Green Island, Taiwan
SV_Oceans_Drop off Tulamben.png
Drop-off, Tulamben, Bali, Indonesia
SV_Oceans_Benwood Wreck.png
Benwood Wreck, Florida Keys, USA
SV_Oceans_Bleached Coral.png
Bleached Coral at Airport Reef, American Samoa
SV_Oceans_Hourglass Reef.png
Hourglass Reef, Bermuda
SV_Oceans_PUE Coral Gardens.png
PUE Coral Gardens, Cook Islands
SV_Oceans_Isla Mujeres.png
Underwater Museum, Isla Mujeres
SV_Oceans_Yamada Point.png
Yamada Point, Onna, Okinawa, Japan

We hope these stories and sights will inspire you to get involved and help protect our vast and fragile oceans. So jump on in, the water’s fine!

Photo credit for header image of Raja Ampat: Jayne Jenkins

Dive into World Oceans Day with Google Earth and Maps

This World Oceans Day, explore our oceans and learn from experts leading marine conservation efforts around the world with Street View and Google Earth.

We hope these stories and sights will inspire you to get involved and help protect our vast and fragile oceans. So jump on in, the water’s fine!Start your underwater adventure in Voyager. With the Ocean Agency, tour Raja Ampat, Indonesia’s rich marine center containing 76 percent of the world’s coral species and 36 percent of the world’s reef fish species—more than any other marine region in the world. Then head to the Gulf of California with Dr. Sylvia Earle to see leaping mobula rays and learn about efforts to combat shark finning. Finally, head south to swim alongside the playful sea lions of the Galápagos Islands, one of the most biodiverse and unique places on the planet.

carousel1_raja1.png
carousel2_raja2.png
carousel3_raja3.png
carousel4_mb1.png
carousel5_mb2.png
carousel6_mb3.png
carousel7_gala1.png
carousel8_gala2.png
carousel9_gala3.png

Once you’ve gotten your sea legs in Voyager, sail on over to Street View for even more beautiful blues. Go tete-a-tete with turtles in the Great Barrier Reef off Heron Island, Australia, snorkel through colorful coral in DA Bai Shah, Taiwan, and enroll yourself in a school of fish in Bali.

SV_Oceans_Heron Island.png
Heron Island, Great Barrier Reef
SV_Oceans_Da Bai Sha.png
Da Bai Sha, Green Island, Taiwan
SV_Oceans_Drop off Tulamben.png
Drop-off, Tulamben, Bali, Indonesia
SV_Oceans_Benwood Wreck.png
Benwood Wreck, Florida Keys, USA
SV_Oceans_Bleached Coral.png
Bleached Coral at Airport Reef, American Samoa
SV_Oceans_Hourglass Reef.png
Hourglass Reef, Bermuda
SV_Oceans_PUE Coral Gardens.png
PUE Coral Gardens, Cook Islands
SV_Oceans_Isla Mujeres.png
Underwater Museum, Isla Mujeres
SV_Oceans_Yamada Point.png
Yamada Point, Onna, Okinawa, Japan

We hope these stories and sights will inspire you to get involved and help protect our vast and fragile oceans. So jump on in, the water’s fine!

Photo credit for header image of Raja Ampat: Jayne Jenkins

Dive into World Oceans Day with Google Earth and Maps

This World Oceans Day, explore our oceans and learn from experts leading marine conservation efforts around the world with Street View and Google Earth.

We hope these stories and sights will inspire you to get involved and help protect our vast and fragile oceans. So jump on in, the water’s fine!Start your underwater adventure in Voyager. With the Ocean Agency, tour Raja Ampat, Indonesia’s rich marine center containing 76 percent of the world’s coral species and 36 percent of the world’s reef fish species—more than any other marine region in the world. Then head to the Gulf of California with Dr. Sylvia Earle to see leaping mobula rays and learn about efforts to combat shark finning. Finally, head south to swim alongside the playful sea lions of the Galápagos Islands, one of the most biodiverse and unique places on the planet.

carousel1_raja1.png
carousel2_raja2.png
carousel3_raja3.png
carousel4_mb1.png
carousel5_mb2.png
carousel6_mb3.png
carousel7_gala1.png
carousel8_gala2.png
carousel9_gala3.png

Once you’ve gotten your sea legs in Voyager, sail on over to Street View for even more beautiful blues. Go tete-a-tete with turtles in the Great Barrier Reef off Heron Island, Australia, snorkel through colorful coral in DA Bai Shah, Taiwan, and enroll yourself in a school of fish in Bali.

SV_Oceans_Heron Island.png
Heron Island, Great Barrier Reef
SV_Oceans_Da Bai Sha.png
Da Bai Sha, Green Island, Taiwan
SV_Oceans_Drop off Tulamben.png
Drop-off, Tulamben, Bali, Indonesia
SV_Oceans_Benwood Wreck.png
Benwood Wreck, Florida Keys, USA
SV_Oceans_Bleached Coral.png
Bleached Coral at Airport Reef, American Samoa
SV_Oceans_Hourglass Reef.png
Hourglass Reef, Bermuda
SV_Oceans_PUE Coral Gardens.png
PUE Coral Gardens, Cook Islands
SV_Oceans_Isla Mujeres.png
Underwater Museum, Isla Mujeres
SV_Oceans_Yamada Point.png
Yamada Point, Onna, Okinawa, Japan

We hope these stories and sights will inspire you to get involved and help protect our vast and fragile oceans. So jump on in, the water’s fine!

Photo credit for header image of Raja Ampat: Jayne Jenkins

It’s Beatlemania in Google Earth

The Beatles' eighth album, Sgt. Pepper’s Lonely Hearts Club Band, first hit the record stores in June 1967. The album was created at Abbey Road Studios and features classics like “Lucy In The Sky With Diamonds” and “With A Little Help From My Friends” in addition to the title track. Fifty years later, Sgt. Pepper remains a fan favorite, and is being re-issued with new mixes and tracks. Today on Google Earth’s storytelling platform, Voyager, we’re celebrating not only this beloved, influential album, but the whole of the Fab Four’s monumental contributions to rock ‘n’ roll.

The global Voyager tour highlights significant moments and places in The Beatles’ career. Their journey starts at Liverpool’s iconic Cavern Club, and ends at Strawberry Fields in New York’s Central Park, where a black-and-white tiled memorial honors the life of John Lennon. You can also fly to the Hamburg nightclub where the band performed almost nonstop for 48 days; the New York theater where they made their historic first live TV appearance in America; and India, where they experienced spiritual awakenings in the Maharishi’s ashram.

Join the Fab Four as they tour the globe with Google Earth’s Voyager.

Header image courtesy of Local Guide Austin Ayodeji

It’s Beatlemania in Google Earth

The Beatles' eighth album, Sgt. Pepper’s Lonely Hearts Club Band, first hit the record stores in June 1967. The album was created at Abbey Road Studios and features classics like “Lucy In The Sky With Diamonds” and “With A Little Help From My Friends” in addition to the title track. Fifty years later, Sgt. Pepper remains a fan favorite, and is being re-issued with new mixes and tracks. Today on Google Earth’s storytelling platform, Voyager, we’re celebrating not only this beloved, influential album, but the whole of the Fab Four’s monumental contributions to rock ‘n’ roll.

The global Voyager tour highlights significant moments and places in The Beatles’ career. Their journey starts at Liverpool’s iconic Cavern Club, and ends at Strawberry Fields in New York’s Central Park, where a black-and-white tiled memorial honors the life of John Lennon. You can also fly to the Hamburg nightclub where the band performed almost nonstop for 48 days; the New York theater where they made their historic first live TV appearance in America; and India, where they experienced spiritual awakenings in the Maharishi’s ashram.

Join the Fab Four as they tour the globe with Google Earth’s Voyager.

Header image courtesy of Local Guide Austin Ayodeji

How maps and machine learning are helping to eliminate malaria

Today is World Malaria Day, a moment dedicated to raising awareness and improving access to tools to prevent malaria. The World Health Organization says nearly half of the world’s population is at risk for malaria, and estimates that in 2015 there were 212 million malaria cases resulting in 429,000 deaths. In places with high transmission rates, children under five account for 70 percent of malaria deaths.

DiSARM (Disease Surveillance and Risk Monitoring), a project led by the Malaria Elimination Initiative and supported by the Bill and Melinda Gates Foundation and Clinton Health Access Initiative, is fighting the spread of malaria by mapping the places where malaria could occur. With the help of Google Earth Engine, DiSARM creates high resolution “risk maps” that help malaria control programs identify the areas where they should direct resources for prevention and treatment.

We sat down with Hugh Sturrock, who leads the DiSARM project and is an Assistant Professor of Epidemiology and Biostatistics in the University of California, San Francisco’s Global Health Group, to learn more about DiSARM’s fight against malaria, and how Google fits in.

As an epidemiologist, why did you choose to focus your efforts on malaria?

I first became interested in 2005, during my undergraduate days at the University of Edinburgh when I worked on a project examining the fungal control of mosquitoes with Professor Andrew Read. I suddenly realized that my research could have a positive impact on people’s lives and from that point on I was hooked. While malaria deaths have decreased dramatically since then, it’s still a huge public health problem.

Which regions is DiSARM targeting first?

We’re piloting DiSARM in Swaziland and Zimbabwe, two regions that are on the cusp of malaria elimination. Between 2000–2014, reported malaria cases in Swaziland decreased by 99 percent, and in 2015, Swaziland reported fewer than 400 local cases. Meanwhile, Zimbabwe achieved a 74 percent decline in reported cases from 2005–2015.

When a small number of cases in a region remain, precise intervention is required to fully eliminate malaria, and DiSARM can help fully close the gap. By focusing our resources more strategically, we can shrink the malaria map and eliminate the disease entirely in these countries.

How does DiSARM use Google Earth Engine to help fight malaria?

If we map where malaria is most likely to occur, we can target those areas for action. Every time someone is diagnosed with malaria in Swaziland and Zimbabwe, a team goes to the village where the infection occurred and collects a GPS point with the precise infection location. Just looking at these points won’t allow you to accurately determine the risk of malaria, though. You also need satellite imagery of conditions like rainfall, temperature, slope and elevation, which affect mosquito breeding and parasite development.
GeoForGood_Disarm_800px_V2.gif

To determine the risk of malaria, DiSARM combines the precise location of the malaria infection,  with satellite data of conditions like rainfall, temperature, vegetation, elevation, which affect mosquito breeding. DiSARM’s mobile app can be used by the malaria programs and field teams to target interventions.

Google Earth Engine collects and organizes the public satellite imagery data we need. In the past we had to obtain those images from a range of sources: NASA, USGS and different universities around the world. But with Google Earth Engine, it’s all in one place and can be processed using Google computers. We combine satellite imagery data from Google Earth Engine with the locations of malaria cases collected by a country’s national malaria control program, and create models that let us generate maps identifying areas at greatest risk.

DiSARM targetting.png
The DiSARM interface gives malaria programs a near real-time view of malaria and predicts risk at specific locations, such as health facility service areas, villages and schools. Overlaying data allows malaria control programs to identify high-risk areas that have insufficient levels of protection and better distribute their interventions.

How are the risk maps used?

The Swaziland and Zimbabwe national malaria control programs use risk maps to help track progress and make decisions about how best to use their resources—for example, where to spray insecticides and where to conduct health promotion campaigns. With this data, they can make these decisions in a matter of minutes, rather than days or weeks. And they have much more precise information about where to target their efforts. They can drill down and direct their spray teams to go to the individual houses most at risk. This technique improves the targeting of interventions, saving money and time for the malaria programs.
DiSARM targetting households.png
DiSARM’s targeting module uses the risk map to prioritize areas for interventions such as indoor residual spraying (IRS), insecticide treated nets (ITNs) and mass drug administration (MDA).

We’ve also developed a mobile app with instructions for field teams and the locations of buildings they need to target on an offline map. They can also use the app to collect data even if they don’t have connectivity while they’re in remote locations.

What’s next for DiSARM?

Over the next year, we’re planning to expand the platform to show not just the current malaria risk, but a forecast for the future. We believe Swaziland and Zimbabwe can eliminate malaria and we hope this tool can get them—and other countries—closer to achieving that goal. To learn more, visit disarm.io.

How maps and machine learning are helping to eliminate malaria

Today is World Malaria Day, a moment dedicated to raising awareness and improving access to tools to prevent malaria. The World Health Organization says nearly half of the world’s population is at risk for malaria, and estimates that in 2015 there were 212 million malaria cases resulting in 429,000 deaths. In places with high transmission rates, children under five account for 70 percent of malaria deaths.

DiSARM (Disease Surveillance and Risk Monitoring), a project led by the Malaria Elimination Initiative and supported by the Bill and Melinda Gates Foundation and Clinton Health Access Initiative, is fighting the spread of malaria by mapping the places where malaria could occur. With the help of Google Earth Engine, DiSARM creates high resolution “risk maps” that help malaria control programs identify the areas where they should direct resources for prevention and treatment.

We sat down with Hugh Sturrock, who leads the DiSARM project and is an Assistant Professor of Epidemiology and Biostatistics in the University of California, San Francisco’s Global Health Group, to learn more about DiSARM’s fight against malaria, and how Google fits in.

As an epidemiologist, why did you choose to focus your efforts on malaria?

I first became interested in 2005, during my undergraduate days at the University of Edinburgh when I worked on a project examining the fungal control of mosquitoes with Professor Andrew Read. I suddenly realized that my research could have a positive impact on people’s lives and from that point on I was hooked. While malaria deaths have decreased dramatically since then, it’s still a huge public health problem.

Which regions is DiSARM targeting first?

We’re piloting DiSARM in Swaziland and Zimbabwe, two regions that are on the cusp of malaria elimination. Between 2000–2014, reported malaria cases in Swaziland decreased by 99 percent, and in 2015, Swaziland reported fewer than 400 local cases. Meanwhile, Zimbabwe achieved a 74 percent decline in reported cases from 2005–2015.

When a small number of cases in a region remain, precise intervention is required to fully eliminate malaria, and DiSARM can help fully close the gap. By focusing our resources more strategically, we can shrink the malaria map and eliminate the disease entirely in these countries.

How does DiSARM use Google Earth Engine to help fight malaria?

If we map where malaria is most likely to occur, we can target those areas for action. Every time someone is diagnosed with malaria in Swaziland and Zimbabwe, a team goes to the village where the infection occurred and collects a GPS point with the precise infection location. Just looking at these points won’t allow you to accurately determine the risk of malaria, though. You also need satellite imagery of conditions like rainfall, temperature, slope and elevation, which affect mosquito breeding and parasite development.
GeoForGood_Disarm_800px_V2.gif

To determine the risk of malaria, DiSARM combines the precise location of the malaria infection,  with satellite data of conditions like rainfall, temperature, vegetation, elevation, which affect mosquito breeding. DiSARM’s mobile app can be used by the malaria programs and field teams to target interventions.

Google Earth Engine collects and organizes the public satellite imagery data we need. In the past we had to obtain those images from a range of sources: NASA, USGS and different universities around the world. But with Google Earth Engine, it’s all in one place and can be processed using Google computers. We combine satellite imagery data from Google Earth Engine with the locations of malaria cases collected by a country’s national malaria control program, and create models that let us generate maps identifying areas at greatest risk.

DiSARM targetting.png
The DiSARM interface gives malaria programs a near real-time view of malaria and predicts risk at specific locations, such as health facility service areas, villages and schools. Overlaying data allows malaria control programs to identify high-risk areas that have insufficient levels of protection and better distribute their interventions.

How are the risk maps used?

The Swaziland and Zimbabwe national malaria control programs use risk maps to help track progress and make decisions about how best to use their resources—for example, where to spray insecticides and where to conduct health promotion campaigns. With this data, they can make these decisions in a matter of minutes, rather than days or weeks. And they have much more precise information about where to target their efforts. They can drill down and direct their spray teams to go to the individual houses most at risk. This technique improves the targeting of interventions, saving money and time for the malaria programs.
DiSARM targetting households.png
DiSARM’s targeting module uses the risk map to prioritize areas for interventions such as indoor residual spraying (IRS), insecticide treated nets (ITNs) and mass drug administration (MDA).

We’ve also developed a mobile app with instructions for field teams and the locations of buildings they need to target on an offline map. They can also use the app to collect data even if they don’t have connectivity while they’re in remote locations.

What’s next for DiSARM?

Over the next year, we’re planning to expand the platform to show not just the current malaria risk, but a forecast for the future. We believe Swaziland and Zimbabwe can eliminate malaria and we hope this tool can get them—and other countries—closer to achieving that goal. To learn more, visit disarm.io.