My Google Map Blog

Réunion, a French Island in the Indian Ocean not far from Mauritius, has recently received Street View. We found that although the coverage shown by the blue lines is quite extensive we were not able to access it in a number of areas. For example, Îlet à Cordes shows blue lines but if you try to enter Street View there in either Google Earth or Google Maps it does not succeed.

Changes to Street View between January 28th, 2016 and February 28th, 2016. Changes marked in red. Existing Street View in blue. Large version

See it in Google Maps . With nothing to judge scale by, this volcano looks deceptively small and close. It is actually about 3 km away and has a crater over a kilometre wide at its peak. The whole thing is sitting inside a much larger crater, the edge of which the photo was taken from.

See it in Google Maps. Réunion has some very steep mountains. We thought this spot looked especially beautiful, with the sun just peeping over the mountain.

Other new Street View includes expansions to coverage in Ecuador, Bolivia, Uruguay, Brazil, The Philippines, Ukraine and Russia. And the Google LatLong blog lets us know about some US stadiums that have received Street View. There is also some new Street View of Ellef Ringnes Island in the far north of Canada. It is dated April 2014, but appears to have only recently been added.

See it in Google Maps. If Réunion was too hot and tropical for you, then try exploring Ellef Ringnes Island in northern Canada.

The post Street View comes to Réunion appeared first on Google Earth Blog.

We recently came across this story about what at first sight appears to be a plane at the bottom of a lake. Today we are looking at how this ‘ghosting’ effect comes about.

The two ‘ghostly planes’ noted in the article above.

We have previously discussed the ‘rainbow effect’, which has even inspired artists. That is caused by the way satellite cameras work, taking multiple photos in quick succession with different colour filters.

However, the planes in this case are in 3D imagery, which is not captured by satellites, but from aircraft. An aircraft flies over a region capturing multiple images in succession. It then comes past again at a later time and captures more images. In this post we noted that a water tower had been photographed from six different directions at two different times of day. Next Google uses an algorithm that uses the stereoscopic effect to reconstruct the 3D shape of objects. However, this fails for moving objects. At airports we often see aircraft that were captured well from one side but had left or moved before the other side was captured and they end up looking hollowed out.

Planes that moved before all the second set of images were captured.

Vehicles have a similar problem:

You can read the number on the roof of the bus, but the rest is a ghost.

The technique used for reconstructing the 3D has particular problems with water. The surface of the water is constantly changing, which confuses the algorithm. Uniform surfaces with no markings also give problems. Google often completely turns off the 3D generation for large rivers, lakes and the sea. However, they still have multiple photos for each location, which the blend together. As we showed you in this post, this can lead to some interesting effects.

Based on carefully studying of a location in Venice, Florida, USA, we believe that there are four images captured in fairly quick succession, followed by another four at a later time, resulting in anchored boats appearing to have eight copies altogether, but moving boats only appear to have four images, unless you can figure out where they were on the second pass. There are also some much less distinct images for each boat, but they are harder to count or work out when they were captured.

Each boat has 8 distinct images as well as other less distinct images.

From the boat track below we can see that there is quite some time between each individual image:

An aircraft in flight, such as the ones at the beginning of the post, is so fast that it will only be captured once and thus appears fainter than most of the other images we have featured above.

To see the locations featured in this post in Google Earth download this KML file. Be sure to turn on the 3D buildings layer to see the imagery.

The post Ghostly planes in Google Earth 3D imagery appeared first on Google Earth Blog.

As we mentioned in yesterday’s post we have been working on a bit of code to determine whether or not a placemark lies inside a polygon. We believe we have got it working and thought it might be useful for people who want to classify placemarks by region. So, we have created the JavaScript-based tool below.

To use it, simply upload a set of placemarks and polygons and it will check what polygons the placemarks are in and put them all in folders named after the polygons and give you back a KML file with all the placemarks nicely categorised. As an example, we found this map of nuclear power plants on the Google Maps Gallery. We opened it in Google Earth to get the KML version, then used both it and the country borders we showed you yesterday to classify all the nuclear power stations by country.

It accepts multiple files so you can have your placemarks in one file and regions in another. It can be quite slow for large datasets. The above datasets took about 3 minutes on a fast computer.
[ Update: We have fixed a few bugs and added some optimizations which has improved the performance significantly.]

Include empty regions

Classify placemarks

As usual we have not yet tested it thoroughly and make no guarantees that it will work perfectly. The country outlines are not very high resolution so placemarks near borders may be incorrectly classified. Let us know about any bugs you find or enhancements you would like to see, in the comments below.

Once the placemarks are classified it is easy to select only one folder.

We were also able to use it to make an approximate list of the number of nuclear power stations per country. Note that the list includes both active and inactive power stations and lists individual reactors not sites. For example Pickering in Canada has 8 reactors. We also do not know how old the list is.

The biggest problem is that nuclear reactors are often near coastlines and as mentioned above, the country outlines are not very high resolution and many of the nuclear reactors are outside the country borders. So, of the 766 reactors in the original file, only 578 were successfully classified.

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