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The amazing things about Google Earth
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Washington, D.C. now has Google Earth 3D

lun 01-08-2016

Google has recently pushed out a number of new 3D areas. Of special note is Washington, D.C., the capital city of the United States of America. Google usually takes its time releasing capital cities as there are often a lot of landmark buildings and bridges in capital cites that Google pays special attention to. With Washington, D.C. we had assumed the delay was likely getting appropriate flying permission to fly over very security-sensitive areas. However, the imagery was captured sometime around October 2014. Not included in the 3D imagery are the most security-sensitive areas, the area known as the National Mall and surrounds, which includes the White House, the Washington Monument, U.S. Capitol Building and the Lincoln Memorial. We don’t know whether this means that it is still being worked on, or will never be added.

When looking around Washington, D.C. we felt that the trees seem to look more realistic than they used to in older 3D imagery. We believe this is because Google has got better at dealing with overhangs in the 3D imagery processing. It is difficult to do comparisons of Google’s progress on 3D imagery, as they don’t date the imagery and many areas have been reprocessed or updated over time. However, there was a water tower in Paris that we looked at a couple of years ago and the improvement is clear. At that time almost all objects with overhangs had vertical sides down to the ground, which caused trees to look like large bushes.

A water tower in Paris, France, as seen in Google Earth 3D. Left: September 2014, Right: as it looks now.

Now, trees and other objects seem to have quite significant overhangs, with the trunk of the tree either visible or non-existent, but still a much better effect than vertical sides.

Although Google still can’t image the underside of trees, the overhang and level of detail of the road under the trees is impressive.

When Google adds 3D to an area they turn off the old type of 3D buildings, and often turn them off for a region around the new 3D area. This is also the case for Washington, D.C., but it seems to have been somewhat selective. The White House, for example, is missing but fountains on either side of it are still there. This is not censorship, as the old 3D models are still available if you switch to ‘historical imagery’.

Also of special interest, parts of Cape Town, South Africa are now in 3D. The Cape Town imagery was captured around January 2015.

To see what’s new see our KML file or for the very latest finds see the comments at the end of this post. Once again, thank you to all those GEB readers who contribute to finding new areas and/or drawing the outlines.

The post Washington, D.C. now has Google Earth 3D appeared first on Google Earth Blog.

Catégories: Sites Anglophones

The best of Google Earth for July 2016

ven 29-07-2016

Google has been adding new imagery to Google Earth, but has not updated historical imagery for over a month, so we are unable to do maps of the new imagery or see any of the added imagery that is not in the default layer.

We had a look at different ways to use AI image recognition with Google Earth imagery. There are a lot of interesting potential applications using the satellite and aerial imagery as well as Street View imagery.

We had a look at ‘rainbow planes’ and explained how the effect is a result of a combination of the satellite’s movement as well as the plane’s movement. We noted that it was possible to use rainbow planes to estimate the altitude of the satellite and use this, combined with the imagery date and imagery supplier, to guess which satellite captured the image. This lead us to create a list of imaging satellites. We also had a look at what sun-synchronous orbits look like and then created a Google Earth Tour animating the Landsat 7 orbit.

We had a go at watching sand dunes move with the aid of historical imagery. The biggest difficulty we had was finding suitable locations, as most deserts have very little historical imagery. We were, however, able to find some examples of sand dunes moving.

We had a look at the last ten years of imagery updates for the continental US and for Europe and used this to estimate how frequent imagery updates can be expected in those regions. (Every three years for the Continental US and five to seven years for Europe.)

We had a look at a Street View tour maker created by Steven Ho. It is a clever idea that allows you to create Google Earth Tours that include Street View sequences.

Tired of waiting for Google to get to the Faroe Islands, some enterprising locals decided to capture their own Street View with the help of sheep. They have named it Sheep View.

We showed you how to process Sentinel Imagery with the help of GIMP. If you decide to try it out be sure to also read this post, which has some more details and a tool to help create the final image overlay. We also had a look at a massive landslide in Alaska with the help of Sentinel imagery.

We had a look at China’s south-north water transfer project, one of the most expensive engineering projects ever undertaken.

We discovered by accident that the backspace key allows you to go back to the previous view in Google Earth. In fact, it remembers everything you looked at for the whole session.

We found some multi-coloured patches of snow in various places that have been introduced as part of Google Earth’s new global moasaic. We believe it has to do with a bug in the way the imagery is processed for the transition between the global mosaic and the higher resolution imagery displayed when zoomed in.

We noticed some extended Street View coverage in Bangladesh and Mongolia, but at the time the Mongolian Street View was not working. That has since been rectified.

With the arrival of NASA’s Juno probe at Jupiter we discussed why a ‘Google Jupiter’ would not work the same way as Google Earth or Google Mars. Jupiter simply doesn’t have a mapable surface. Google Jupiter would be closer to a weather map than a ground map.

We created a tool that makes use of the Google Earth API to check whether placemarks have imagery after a given date. This is useful if you have a large number of placemarks and you want to check for recent imagery in their locations.

A number of readers reported that the Weather layer in Google Earth is broken. It only affects the ‘Conditions and Forecasts’ layer. It is still broken, with the exact same data showing for the places we looked at when we wrote the post.

While trying to determine how recent the imagery in Google Earth’s new global moasaic is, we created an animation of the shrinking Aral Sea.

The post The best of Google Earth for July 2016 appeared first on Google Earth Blog.

Catégories: Sites Anglophones

Image recognition and Google Earth

jeu 28-07-2016

The last few years have seen major advances in computer artificial intelligence (AI). One area where AI is starting to show practical use is in imagery recognition. Google Earth and Street View imagery combined with image recognition has a wide range of possible applications. We have in the past had a look at Terrapattern, an experimental search engine for aerial and satellite imagery. They are adding new areas with time, so be sure to keep an eye on them.

We recently came across this story about a Caltech researcher that is helping the city of Los Angeles to count its trees with the help of a combination of Google Earth imagery and Street View. In this case they are trying to not only count individual trees but also identify the species.

The idea of using imagery for surveys of vegetation is of course far from new. Google Earth Engine, for example, is designed around such large scale analysis. When you wish to simply determine whether there is vegetation cover or possibly the overall health of the vegetation, a much better option than Google Earth imagery is to use false colour imagery – and satellites are typically designed with this in mind.

Another example of people using image recognition on Street View imagery is this one about identifying fire-hydrants and mentioned in that article is a project using Street View to study gentrification, which uses historical Street View to measure changes in buildings over time.

There is also this project, which uses Street View to geolocate an image. You could potentially take a photo with your mobile phone camera and the system could tell you where you were with accuracy similar to GPS. At present, this sort of thing is often done by crowd-sourcing rather than an automated system. The potential for automated systems has both potential benefits and serious privacy concerns.

Google itself applies some image recognition to Street View. The best known is identifying licence plates and faces, which are blurred for privacy reasons. However, it also reads house numbers and various street signs, and this information is used to improve Google Maps.

If Google were to add infrared to their Street View cameras, maybe it would make it easier to distinguish between faces of people who need privacy and faces of statues who need publicity.

Having infrared Street View has other uses and has been thought of already.

The post Image recognition and Google Earth appeared first on Google Earth Blog.

Catégories: Sites Anglophones

Another way to visualise sun-synchronous orbits

mer 27-07-2016

Yesterday we had a look at the orbits of imaging satellite’s from the perspective of a stationary earth. Today we are having a look at the same orbits but showing how the orbit is actually a circle with the earth rotating inside it.

We found a model of Landsat 7 on the sketchup 3D warehouse and have created a tour showing what Landsat 7’s orbit looks like. The satellite is not shown to scale but the orbit should be approximately correct. Landsat 7 crosses the equator from north to south at about 10:00 am every 98.83 minutes (yes, it’s confusing). Its orbit covers the entire earth every 16 days and then repeats.

One problem we have encountered is animating a model across the antimeridian does not work correctly in Google Earth. We have not yet found a work-around. You will notice the model appears to jump occasionally when crossing the antimeridian. Another bug is that the background of stars shakes around when playing the tour. The stars should be stationary relative to the view, as the satellite’s orbit is nearly stable with respect to the stars, drifting approximately 1 degree per day (360 degrees per year).

Here we see Landsat 7’s orbit over the course of 24 hours:

You can view it in Google Earth with this KML file. For best results turn on sunlight (the icon with a rising sun on the toolbar). We also include in the KML the orbit for 24 hours or for the full 16 days.

Note that Landsat 8 shares the same orbit but with an 8 day offset.

This is what its 16 day orbit looks like relative to the earth:

We couldn’t record the full 16 day orbit as a tour as Google Earth couldn’t handle it. We believe it is possible to use a KML feature called a Track to improve performance, but we have not yet figured out how to do that.

For comparison, here is the layout of imagery tiles that are captured by Landsat 7 as provided by the USGS:

The post Another way to visualise sun-synchronous orbits appeared first on Google Earth Blog.

Catégories: Sites Anglophones

Sun-synchronous orbits with Google Earth

mar 26-07-2016

After our recent posts on rainbow plane offsets and the list of imaging satellites we thought it would be interesting to see what a sun-synchronous orbit actually looks like in Google Earth. We have previously written a post about sun-synchronous orbits and why most imaging satellites use them, but we only showed an approximate single orbit of the earth assuming the earth was not rotating. In reality, the earth rotates on its axis as well as going around the sun. A sun synchronous orbit is designed to drift slowly so as to keep in sync with the earth’s orbit around the sun. All this starts to get complicated when you want to plot an actual orbit. But we believe we have succeeded.

What the orbit of WorldView-3 looks like.

We used the equations from Wikipedia, which allow us to use the altitude and period of a given satellite to work out its orbit. For simplicity we start at latitude zero and longitude zero. We show the orbit for a period of approximately 24 hours. Some satellites, such as Landsat 8, have their orbits arranged so that they repeat the same path on a regular basis. Others do not.

All sun-synchronous orbits look very similar, with the differences in altitude being hardly noticeable. The most obvious difference is in the period, which affects the spacing of the orbits.

Red: Landsat 8 orbit. White: WorldView-3 orbit.

WorldView-3 has an altitude of 617 km and a period of 97 minutes.
Landsat 8 has an altitude of 703 km and a period of 98.8 minutes.

The diagonals from the north east to south west ( / ) are on the daylight side of the earth and the diagonals from south east to north west ( \ ) are on the night side.

To see the above orbits in Google Earth download this KML file. Alternatively, you can create your own orbit by entering the altitude and period below:


Sun-synchronous orbit creator.

Altitude: km

Period: minutes

Create orbit

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Catégories: Sites Anglophones

Imaging satellite list

lun 25-07-2016

Last week we mentioned that it would be useful to have a table of imaging satellites with some of the specifications of most interest to users of Google Earth. We couldn’t find any site with a single list in table form, so we decided to create our own.

The information comes from three main sources: Wikipedia, DigitalGlobe’s website and Sat Imaging Corporation’s website. We make no guarantees about the accuracy of this data.

.satelliteList th{ font-weight:bold; padding:4px; } .satelliteList td{ padding:4px; } .satelliteList{ font-size:12px; } .future{ color:#2980B9; } .old{ color:red; } h2{font-size:1rem} Satellite Company Panchromatic
Resolution Multispectral
Resolution Launch
Date Decommissioned Altitude GeoEye-1 DigitalGlobe 46cm 1.64m 2008-09-06 681 / 770 WorldView-1 DigitalGlobe 50cm – 2007-09-18 496 WorldView-2 DigitalGlobe 46cm 1.85m 2009-10-08 770 WorldView-3 DigitalGlobe 31cm 1.24m 2014-08-13 614 WorldView-4 DigitalGlobe 31cm 1.24m 2016-09 617 IKONOS DigitalGlobe 80cm 3.2m 1999-09-24 2015-04 681 QuickBird DigitalGlobe 55cm 2.16m 2001-10-18 2014-12-17 400 Pleiades-1A CNES 50cm 2m 2011-12-17 695 Pleiades-1B CNES 50cm 2m 2012-12-02 695 KOMPSAT-3A KARI 55cm 2.2m 2015-03-25 543 KOMPSAT-3 KARI 70cm 2.8m 2012-05-17 533 Gaofen-2 CAST 80cm 3.2m 2014-08-19 631 TripleSat (3 satellites) SSTL 80cm 3.2m 2015-07-10 SkySat-1 Terra Bella 90cm 2.0m 2013-11-21 450 SkySat-2 Terra Bella 90cm 2.0m 2014-07-08 450 SPOT-1 CNES 10m 20m 1986-02-22 1990-12-31 832 SPOT-2 CNES 10m 20m 1990-01-22 2009-07-01 832 SPOT-3 CNES 10m 20m 1993-09-26 1997-11-14 832 SPOT-4 CNES 10m 20m 1998-03-24 2013-07-01 830 SPOT-5 CNES 2.5m 10m 2002-05-04 2015-03-31 832 SPOT-6 CNES 1.5m 6.0m 2012-09-09 832 SPOT-7 CNES 1.5m 6.0m 2014-06-30 832 Landsat 1 USGS 1972-07-23 1978-01-06 Landsat 2 USGS 1975-01-22 1982-02-25 Landsat 3 USGS 1978-03-05 1983-03-31 Landsat 4 USGS 1982-07-16 1993-12-14 Landsat 5 USGS 1984-03-01 2013-06-05 Landsat 6 USGS 1993-10-05 1993-10-05 Landsat 7 USGS 15m 30m 1999-04-15 702 Landsat 8 USGS 15m 30m 2013-02-11 702 RapidEye (5 satellites) Planet Labs 5m 630 Doves (many satellites) Planet Labs approx 400 ASTER Japan METI 15m 15m 1999-12-18 705 Sentinel 2A ESA 10m 10m 2016-06-23 768 Sentinel 2B ESA 10m 10m 2016 768

Red: No longer active.
Blue: Not yet launched. Notes on the above data:

Most of the satellites capture imagery in high resolution in grayscale (panchromatic) and lower resolution in each of the three primary colours and the images are combined to produce a high resolution colour image.

Orbits are typically not perfectly round and the earth itself is not perfectly spherical, so altitudes listed are averages. GeoEye-1 apparently had its orbit changed to a higher orbit in 2013.

Details not listed.

As far as we know, all the satellites can capture imagery in various infra-red bands that are typically lower resolution than the colour bands. There is significant variation between satellites as to which infrared bands they can capture.

There are differences in how the satellites capture imagery, such as whether they use a single sensor, a row of sensors or a sensor array.

Most satellites capture imagery in strips, which have a specific ‘swath width’ for a given satellite.

The best quality image is usually captured straight down from the satellite (nadir) but most satellites are capable of rotating so as to be able to capture imagery to either side of their orbit. How fast they can rotate varies by satellite.

Most satellites have a limit on how much imagery they can capture at a time. This is due to data storage and communication limitations.

Satellites vary as to how accurately they can report exactly where they were and what direction they were pointing when they took the photo.

The mass of the satellites varies considerably. In some cases the ‘satellite’ listed is actually an instrument on a larger satellite (ASTER for example).

Each satellite crosses the equator at a specific time.

Satellite design.

The various different requirements for satellites are
1. Resolution.
2. Acquisition speed. (How quickly a satellite can image a given area when required such as after a natural disaster).
3. Repeat acquisition. (The frequency with which it can image a given location).
4. Area covered. (Higher resolution typically means smaller coverage footprints, so lower resolution can be an advantage at times).
5. Consistency. (This is important for long term environmental monitoring).
6. Global coverage. (The ability to image the whole globe on a regular basis).
7. Price.

Commercial satellites tend to be more interested in 1. to 4., whereas government programs are more interested in 5. and 6. DigitalGlobe’s satellites are mostly expensive high resolution satellites whereas Planet Labs has sacrificed resolution in order to be able to make large numbers of cheap satellites, which allows them to cover more area quicker and repeatedly. The Landsat, SPOT and Sentinel satellites are designed for long term, regular, consistent coverage for environmental monitoring. The Sentinel satellites are essentially a continuation of the SPOT satellites with their orbits chosen to match.

Where does Google Earth imagery come from?

The main suppliers of satellite imagery to Google Earth at the current point in time are Digital Globe and CNES/Astrium, so almost all satellite imagery comes from their satellites. The low resolution global mosaic is Landsat 7 / 8 imagery, which is also used to fill in the gaps where there is no higher resolution imagery. Imagery from the SPOT satellites is also used for filling in gaps and can be recognised from the copyright notice “CNES/Spot Image”.

South America has a lot of imagery dated 1970. We are not sure which satellite it comes from, but we guess it may be from one of the early Landsat satellites and the date is only approximate.

Landsat, ASTER and Sentinel imagery is free to the public and can be downloaded and viewed in Google Earth.

Google Earth includes aerial imagery (captured from aircraft not satellites) from a number of sources.

Landsat 8. Image from Wikimedia commons.

The post Imaging satellite list appeared first on Google Earth Blog.

Catégories: Sites Anglophones

Rainbow plane offsets

ven 22-07-2016

When we had a look at the ‘rainbow effect’ of planes in flight we mentioned that the offsets of the different images were a result of both the plane’s movement and the movement of the satellite taking the photos. We thought it would be worth having a look at that in more detail.

We know that most imaging satellites follow a sun-synchronous orbit. It is fairly easy to approximate that orbit relative to a plane in flight by drawing a line from the location of the plane to form a tangent on the right hand side of the North pole with the circle of latitude 82° N. For increased accuracy we will try to follow the tail of the plane.

In the image above, the satellite was travelling north to south in the direction of the red line although not necessarily directly overhead.

The satellite captured 4 photos: one high resolution grey scale image which we see at (1), and then after a short delay, blue, red and green images in quick succession which we see overlapping at (3).

If the plane was completely stationary, we would have expected the rainbow images to appear at (2) due to parallax and the motion of the satellite. If the satellite was stationary and the plane moving, then we would expect to see them at (4).

Using Google Earth’s measuring tools the distance from (1) to (2) is about 40 m. The distance from (2) to (3) is about 70 m.

This is enough information such that if we knew which satellite took the image, and how long it pauses between the monochrome and colour images, we could work out the approximate altitude and velocity of the plane. Alternatively, if we knew the altitude and velocity of the plane, we could, work out which satellites could have taken the image.

One useful fact is that all the possible satellites have very similar velocities which we can approximate at 7.5 km/s.

Wikipedia suggests that a typical plane cruises at 878–926 km/h at an altitude of around 12km and that a much higher altitude is not possible.

So, if we start by guessing the planes altitude at 12 km, its velocity at 900 km/h then we get the satellite altitude at about 550 km. Now we look through this list to try and find a matching satellite from DigitalGlobe – keeping in mind the image was captured in 2012 so satellites launched after that date must be excluded. Our best guess is that the image was probably captured by a satellite in a slightly lower orbit such as World-View-1 at an altitude of 496 Km and to make our calculations match up, the plane was probably at a slightly lower altitude of 11 km above sea level. (The ground at this location is 1.3 km above sea level.)

The time between the the monochrome and colour images is about 0.27 seconds.

If any of our readers knows of a reference with satellite orbit data in tabular form for a wide variety of imaging satellites please let us know in the comments.

We also came across this image:

The images of the plane appear to have been sheared and offset slightly in a horizontal direction, but the image of the ground does not seem to have been affected. We don’t know how this happened. Do our readers have any suggestions? One of the images in our earlier post on the ‘rainbow effect’ also includes an plane which seems to have a double tail which may be a related effect.

To find the above planes in Google Earth download this KML file.

We found the above planes using the Google Earth Community aircraft in flight list.

The post Rainbow plane offsets appeared first on Google Earth Blog.

Catégories: Sites Anglophones

Watching sand dunes move with Google Earth

jeu 21-07-2016

We recently got the idea of trying to see if we could see sand dunes moving using Google Earth historical imagery. The biggest problem is that for the best results we needed frequent satellite imagery over as long a time period as possible, but most deserts have very little satellite imagery. Google Earth imagery tends to focus on populated areas, so we looked for towns that have sand dunes on the outskirts. We started with Nouakchott, the capital of Mauritania. We had come across this article, which suggests that Nouakchott may be slowly obliterated by creeping sand dunes. But what we found, in the places we looked, was that the opposite was the case – the city was slowly taking over the sand dunes.

We recommend watching the videos full screen.

We tried a little to the east of Nouakchott and, since the image jumps around quite a lot due to poor image alignment in Google Earth, we cannot definitively say which way the dunes are moving, if at all. They do change shape quite considerably in the first few frames:

Next we tried Namibia and chose some sand dunes just east of Lüderitz. This time there is no doubt that the dunes are moving northwards.

We also looked at sand dunes east of Oranjemund, also in Namibia. There isn’t much imagery and it gets updated in sections, but the overall movement is still clear. If it wasn’t for the town of Oranjemund staying in one place you might think the sand was stationary and the imagery was just being moved.

Still in Namibia, we go to Walvis Bay and here we can see dunes slowly moving north-west.

You can find some more videos we made of dunes near Dubai, UAE, that were less successful here and here.

You can download the Google Earth tours used to create the above videos.

To create the above videos we used our advanced Google Earth historical imagery tour maker and Google Earth Pro’s built-in Movie Maker.

We presume that how fast sand dunes move depends on many factors, including wind speeds, dune size and grain size.

The post Watching sand dunes move with Google Earth appeared first on Google Earth Blog.

Catégories: Sites Anglophones

How often is Google Earth imagery updated: Europe

mer 20-07-2016

We recently had a look at the frequency of imagery updates for the continental US. Today we are doing a similar analysis for Europe.

As with the US, Europe has a lot of aerial imagery gathered by the various countries or local companies and later obtained by Google. Also, as with the US, Google has been gathering its own aerial imagery in recent years. In general, most Western European countries have good aerial imagery coverage with the exception of Ireland and the northern parts of Norway, Sweden and Finland. Meanwhile most Eastern European countries have only satellite imagery. This does tend to result in Eastern Europe seeing more regular updates, although it is lower quality imagery.

Keep in mind that actual imagery is smaller than the areas shown and the maps show large sections in the sea that are due to a bug in the way the Google Earth API reports imagery.

Speed in milliseconds per image:

The are no clearly discernible patterns in the yearly data and we estimate complete coverage to be taking about five to seven years, which is generally poorer than the US, which is every three years. The UK is an exception, with noticeably poorer coverage in recent years.

2016 has seen very few updates in Western Europe, but we believe that is because of the seasonality of aerial imagery. We can probably expect to see significant additions in the coming months as aerial imagery gathered in the summer starts to be added. Eastern Europe’s satellite imagery is processed faster and is less restricted to the summer months.

To see the data in Google Earth download this KML file.


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Catégories: Sites Anglophones

Street View Tour Maker by Steven Ho

mar 19-07-2016

Steven Ho, whose excellent work we have covered many times in the past, has recently produced a tool for creating Street View tours. The tool and details on how to use it can be found on his blog here. The instructions for how to use it are a little hard to follow as English is not Steven’s first language, but he provides a number of examples so if you wish to use the tool, it shouldn’t be too difficult to figure out how to use it.

In addition to a number of samples produced with the tool provided in the above post, he has also used it to create a tour of Kumamoto Castle, Japan which was severely damaged in the Kumamoto Earthquakes in April, 2016. See part of that tour in the YouTube video below and read more about it on his blog.

The tours he has created can be played back in Google Earth, but due to a bug in Google Earth they do not always enter Street View automatically. If this happens he suggests pausing the tour and manually entering Street View by dropping the yellow man on the map before continuing.

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Catégories: Sites Anglophones

Sheep View

lun 18-07-2016

Google Street View coverage has been increasing at a steady pace for the last few years, with new countries being added every few months. There are, however, still many places that do not have Street View and feel left out. One such place is the Faroe Islands, which are situated north of Scotland about halfway between Norway and Iceland. A group of Faroe Islanders decided to start a campaign #WeWantGoogleStreetView but rather than simply wait for Street View they started capturing their own Street View with the help of other residents of the Faroe Islands – the sheep. Read more about it here. Also see their website SheepView360 and the YouTube video below:

On the SheepView360 website there are some 360° YouTube videos where you can look around while watching the video or pause it and look around as if it is Street View. This makes us wonder whether such features will one day make it into Street View. Would 360° videos improve the Street View experience or not work that well?

We only managed to find one Sheep View image in Street View, but it is a new project, so we expect many more will follow.

See it in Street View

In some places, the reason Google has not collected Street View has to do with governments not allowing it for various reasons. Germany put a halt to Street View collection over privacy concerns (some parts of Germany do have Street View) and India has also so far refused to allow it on a large scale (some Street View exists) over security concerns. Submissions by individuals however do not face the same legal obstacles so there is nothing stopping you from adding to Street View in those countries.

Currently, user submitted photos do not work in Google Earth’s Street View so there is no way to see Sheep View in Google Earth. Hopefully Google is working on a fix for this.

The post Sheep View appeared first on Google Earth Blog.

Catégories: Sites Anglophones

More about processing Sentinel imagery

ven 15-07-2016

We recently had a look at how to process Sentinel imagery using GIMP. GEB reader ‘DJ’ asked in the comments if the geodata supplied with the imagery can be used to automatically align the imagery in Google Earth, rather than the manual method we had suggested. So we decided to investigate. We had initially thought the geodata could easily be extracted from a file called metadata.xml that is supplied on AWS with the imagery, but it turns out that although that file does contain the geodata it is not the straightforward latitude and longitude of the images. Instead, the coordinates are supplied in the Universal Transverse Mercator coordinate system. There is also a lot of other information, such as the angle of the sun relative to the ground at any given point and the angle relative to the ground with which the satellite camera was viewing it.

In addition to the metadata.xml file, it turns out the ‘.jp2’ files also contain geodata in Geography Markup Language (GML), again using UTM for the coordinates. If you open the ‘.jp2’ files in a text editor you can see the GML data. There is also a file called tileInfo.json that again contains the coordinates in UTM format.

We decided it wasn’t worth the effort of trying to convert the UTM coordinates into latitude and longitude, as the KML file for determining tile codes already has all the information we need. So we made this JavaScript tool that accepts a tile code and creates a KML Ground Overlay with the correct coordinates and you can then open its properties in Google Earth and select the Sentinel image you have created with GIMP. Note that the corners will be correct, but we found that the imagery in other parts of the image may not line up exactly with Google Earth imagery. We believe GeoSage’s Spectral Transformer for Sentinel-2 Imagery is able to stretch the image using the information in metadata.xml for a more accurate result, but we could not confirm this as our trial licence has expired. For most casual uses, however, our method should be good enough.

Also of note is that the AWS files include a file called preview.jpg that is well worth checking before anything else, as you may find there is too much cloud cover, or the image doesn’t cover the part of the tile you are interested in, etc.

It is important to note that the Sentinel imagery has a resolution of 10 m per pixel, which is better than Landsat imagery but not as good as the high resolution satellite and aerial imagery available for most locations in Google Earth. So the main use of Sentinel imagery is for observing large scale events that are not yet visible in Google Earth. It is also good enough to see developments like road construction, deforestation or mining in areas where Google Earth has no recent imagery.

False Colour
It is also relatively easy to create false colour images. To do this, just download extra bands and substitute them when combining in GIMP. For example, one of the more popular false colour combinations is to use the near-infrared band B08 as Red, the Red band B04 as Green and the Green band B03 as Blue. This is a good combination for seeing fire scars. We tested it on the area around Lake Erskine, California the site of the largest, most destructive wildfire of the 2016 California wildfire season.

Copernicus Sentinel data, 2016.
1. The scar from the Erskine fire (blackened area).
2. and 3. show two other small fire scars.

Note that the other bands are lower resolution. See this page for details on the different bands.

Here is the same area done with band B8A, B11 and B12:

The post More about processing Sentinel imagery appeared first on Google Earth Blog.

Catégories: Sites Anglophones

China’s South–North Water Transfer Project

jeu 14-07-2016

We recently came across China’s South–North Water Transfer Project. China has more water in the south than the north and water shortages are becoming a problem in the north. The South–North Water Transfer Project uses canals to move water from large rivers in the south to northern provinces. The project consists of three main routes:

  • The Eastern Route that makes use of China’s ancient Grand Canal for some of its length and started operating in December 2013.
  • The Central Route that started operating in December 2014.
  • The Western Route that is still very much in the early planning stages.

With $79 billion already spent on the project by 2014 it is one of the most expensive engineering projects in the world.

We thought it was such a large project that a map of the routes would be easy to find, but surprisingly enough, the Wikipedia page is somewhat out of date and we couldn’t find any maps showing the completed routes. The best we could find were rough sketches drawn before the completion of the Eastern and Central routes.

We thought it would be interesting to trace the routes in Google Earth. We found the Central route without difficulty and it is easy to follow over its length of over a thousand kilometres. It is an enormous project, having to cross over or under major rivers along its route. It also goes through mountains with tunnels often several kilometres long.

The Central Route starts from the Danjiangkou Reservoir, which had to be raised by 13 metres, displacing some settlements along its shores.
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Before and after of a settlement that had to be relocated because of the rising waters.

To get an idea of the scale of the project, this YouTube video shows some of the construction of the Central Route.

The Eastern Route was much harder to trace, as the area has a lot of waterways, including rivers and canals used for shipping and it is not easy to figure out which ones are also being used to move water to the north.

We also came across complicated junctions, such as this one:

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According to Wikipedia the tunnel under the Yellow River consists of two 9.3 m diameter horizontal tunnels, positioned 70 m under the riverbed.

The tunnel under the Yellow River is nearly 5 km long.

You can see below the routes we were able to map out. We are not certain we have got all the Eastern Route sections correct and there are some gaps. The yellow sections are not navigable and are therefore definitely intended for moving water. The orange section is not part of the project, but is a section of the route of the Grand Canal, which extends further south than the water transfer project.

To see the routes in Google Earth download this KML file.

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Catégories: Sites Anglophones

Processing Sentinel imagery with GIMP

mer 13-07-2016

Sentinel imagery can be thought of as Europe’s equivalent of Landsat imagery. It is freely available just like Landsat imagery, but higher resolution. Today we are having a look at how to process it in order to view it in Google Earth with the help of GIMP.

Before we begin, if you intend to work with Sentinel imagery a lot, then first have a look at GeoSage’s Spectral Transformer for Sentinel-2 Imagery, as that makes the process extremely easy and adds some additional features that we simply cannot accomplish with GIMP. The only downside is it is not a free product.

In addition, the European Space Agency (ESA), provides a free tool called SNAP for processing Sentinel imagery, but we have not yet managed to figure out how to use it to get imagery into Google Earth.

Obtaining the imagery
The best way we have found for getting Sentinel imagery is from Amazon Web Services (AWS). The first step is to find out what the tile code is for the area you are interested in. To do this, download this KML file, which shows the tiles and tile codes. In our case we were interested in a landslide that occurred near Glacier Bay, Alaska on 28th June, 2016. This turned out to be tile 08VLL. The next step is to go to this page on AWS and find the data for your chosen tile. So in our case, select ‘8’ which represents the ’08’ in the tile name, next select ‘V’ and finally ‘LL’. Then you choose the date you are interested in, in year – month – day order. Imagery is captured about once a week, but it can vary by location. In our case, the only image so far captured after the event of interest was captured on July 11th, 2016. Finally click on the ‘0’ as there is typically only one image for a given day. You should now see a list of files available, and for a standard colour image you only need B02.jp2, B03.jp2 and B04.jp2. Download them by clicking on the links. Each one is about 85 MB.

The imagery can also be obtained here, which provides the imagery in a format suitable for use with SNAP, but the downloads are typically 5 to 6 GB as they include a large area and all the colour bands.

Converting to jpg
The Sentinel imagery is provided in a format known as JPEG 2000 with file extension “.jp2”. Although the JPEG 2000 standard was created in 2000, it hasn’t been very popular and not many programs support it. We believe GIMP has partial support, but it was not able to open the Sentinel imagery. So, we used a free image viewing program called Irfanview to do the conversion. Simply open the files in Irfanview then save them again as “.jpg”. Other free converters exist such as OpenJPEG and ImageMagick, both of which are command line converters.

Combining the colour bands
The next step is to open all three images in GIMP – open one first, then add the others as layers by dragging them into the ‘layers’ pane. To combine them into a single image, select Colors->Components->Compose. Choose RGB as the colour model and select B04 as the red channel, B03 as the green channel and B02 as the blue channel. This will open a new GIMP window with the three layers combined into a single image. It may still look a bit colourless at this stage. Now select Colors->Levels. In the popup window click the ‘auto’ button, then click ‘OK’. The colours should now look a lot better.

At this point our image looked like this:

Glacier Bay, Alaska, Copernicus Sentinel data, 2016.

Note that the image doesn’t fill the whole square and it is actually only part of a much larger image. However, even this piece is larger than we actually want. So, we cropped the image to the area we were interested in, then exported it as a “.jpg”.

Importing into Google Earth
When you use GeoSage’s Spectral Transformer for Sentinel-2 Imagery, as mentioned earlier, the resulting image contains the geographical coordinates and it can simply be drag and dropped into Google Earth Pro. However, our method above does not include any geolocation information, so it must be manually positioned. Open Google Earth, navigate to the approximate location the image was captured then add an image overlay. In the image overlay properties select the file previously created with GIMP. Now adjust the transparency slider (found just below where you selected the image) to about half way, so you can see both the image you are adding and the Google Earth imagery behind it. The default settings allow you to rotate and adjust the size of the image overlay, but force it to remain a rectangle. However, our Sentinel image is typically not exactly rectangular, so go to the ‘location’ tab in the overlay’s properties window and click ‘Convert to LatLngQuad’. This changes the way you adjust the overlay so that you can now move each corner individually. It can be a little difficult to get it just right, but patience usually pays off in the end. Moving each corner adjusts the whole image and puts out of alignment parts that had already been aligned. You need to look for easily recognisable features as close as possible to each corner then match up the overlay with the Google Earth imagery at each corner in turn and repeat several times until they all match. Once you are done positioning it, put the transparency slider back to the right, so that the overlay is no-longer see-through.

Once aligned, this is what our image looked like in Google Earth:

Glacier Bay, Alaska, Copernicus Sentinel data, 2016.

Zooming in to the location of the Landslide:

Landslide near Glacier Bay, Alaska, Copernicus Sentinel data, 2016.

We can also use Google Earth’s measuring tools to find that the area affect by the landslide is about 10 km in length.

To see the above image in Google Earth download this KML file. To get an idea of the size of the event, look at the northern edge of the overlay. There are two cruise ships visible, one in the Google Earth imagery and one in the Sentinel image. They look tiny in comparison to the landslide. If the landslide had gone into the water it could have caused a catastrophic tsunami.

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Catégories: Sites Anglophones

The “backspace key” in Google Earth

mar 12-07-2016

We recently accidentally pressed the Backspace key in Google Earth and discovered that it takes you back to the previous ‘view’ and even pops up a little help window to tell you what it did and also suggests Ctrl-Backspace has the opposite effect i.e. if you had pressed backspace and gone back to a previous view, Ctrl-Backspace will take you ‘forward’ to the view you just came from.

The interesting thing is that we couldn’t find this keyboard shortcut in any lists of keyboard shortcuts for Google Earth.

We did some experimenting and found that Google Earth remembers everything you have looked at in a given session. A ‘view’ counts as when the earth is stationary in the viewing pane. So, if you move around and zoom in and out stopping between each movement then Google Earth records each pause as a ‘view’. Pressing Backspace repeatedly takes you back through all the things you have looked at. Then if you don’t click anything, you can use Ctrl-Backspace to take you forwards again through the same sequence.

We found that in ‘historical imagery’ it is able to remember what date you were looking at. However, Ctrl-Backspace does not. This is a pity, as it would have made switching back and forth between dates in ‘historical imagery’ much easier. It also does not automatically switch between ‘historical imagery’ and normal view. In addition, it doesn’t remember other settings, such as what layers were selected, etc.

We closed the little help window telling us about the functionality and could not get it back without editing the registry:
HKEY_CURRENT_USER\SOFTWARE\Google\Google Earth Plus\UndoViewNotificationShown=true

For it to work, it requires the ‘map’ area to be selected. So if, for example, you click something on the tool bar, such as switching to ‘historical imagery’, backspace will no longer work until you click on the map again. Also, if you have a placemark selected, backspace will offer to delete the placemark rather than the above functionality.

If any of our readers knows any other shortcuts not found on this list please let us know in the comments.

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Catégories: Sites Anglophones

How often is Google Earth imagery updated: The continental US

lun 11-07-2016

We are often asked how often Google updates the imagery in Google Earth. The answer depends on where you live and can be anywhere from once a week to never. For much of the world, there are certain hotspots that get fairly regular updates and other places that have no high resolution imagery whatsoever. To get an idea of where the hotspots are, see our posts on historical imagery density.

The continental US is a special case as it has complete coverage with aerial imagery that is updated over time in large patches that are not concentrated around particular points of interest. The US government has had various aerial imagery gathering programs in the past whose imagery Google has since acquired and put into Google Earth. These appear to have often been done state by state, or at least the imagery Google acquired often follows state boundaries. More recently, however, Google has had its own imagery collection program. So, we decided to see whether or not they are managing to cover the whole of the US and just how long that takes. We used the Google Earth API to map out imagery of the continental US for the last 20 years.

Speed in milliseconds per image:

And if you wish to see the data in Google Earth, download this KML file.

Keep in mind that the areas shown are slightly larger than the actual imagery.

We believe Google started gathering imagery around 2012 and the change in patterns is noticeable in the above animation. Instead of following state boundaries, Google appears to gather imagery in what appears to be a random pattern. Analysing the data from 2012 onwards, we believe Google is covering the whole country roughly every three years. There is just one little area in Nevada that seems to have been missed.

We must keep in mind that if you are using Google Maps when there is 3D imagery available, that is what is shown, and that is not included in our analysis here, as the dates and extents of 3D imagery is not available via the Google Earth API.


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Catégories: Sites Anglophones

Multi-coloured snow

ven 08-07-2016

Last week Google gave the Google Earth global mosaic a makeover. When we were having a look at some of the minor issues with the new mosaic we noted some odd patterns in Chandeleur Sound, Louisiana. We have since found what we believe are related patterns in other parts of the world where there is permanent snow cover.



The effect occurs as you zoom in and Google Earth is transitioning between the global mosaic created from Landsat imagery, to higher resolution imagery. We believe that it is a glitch in the algorithm used to do the blending between the two imagery sets. The most obvious cases are rectangular sections with very bright colours, but we believe the yellowish tint to the imagery in the right hand side of the screenshot below is also related. The tint goes away as you zoom in further (as do the brighter coloured patches).


In Iceland, there are patches of yellow:


Chandeleur Sound, Louisiana.

To find the locations above in Google Earth download this KML file.

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Catégories: Sites Anglophones

Street View extended coverage and problems: Bangladesh and Mongolia

jeu 07-07-2016

It’s been a couple of months since Kyrgyzstan got Street View, so we thought we would have a look at the changes since then. As you can see below, the main additions are a near doubling of area covered in Bangladesh and Mongolia.

Street View changes between May 9th, 2016 and July 7th, 2016. Red: new, Blue: previously existing. Large version.

However, we found that as of this writing, it is impossible to actually view the new Mongolia Street View. We tried with both Google Earth and Google Maps, both of which show the blue lines, but when you drop the ‘yellow man’ on the map, Google Earth just takes you to ‘ground level view’ and Google Maps does nothing. The older Mongolian Street View works, as does the fresh Bangladeshi Street View.

We also came across a great example of what to do if you just can’t wait for the Street View car. Notice the trail of blue dots in the screenshot below?

Google Maps user Wahidur Rahman has travelled along the road by bus and every so often captured a panoramic photo and uploaded it to Street View. It was overcast, so the lighting was poor and the imagery isn’t as high resolution as typical Street View, but it is certainly better than nothing.

See in Google Maps

However, Google Earth does not currently show user-contributed Street View, so the above imagery can only be viewed in Google Maps. It used to work in the past, but appears to have been broken when Google merged Google Views with Street View.

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Catégories: Sites Anglophones

Why “Google Jupiter” would have to be different

mer 06-07-2016

The big astronomy news this week is the arrival of NASA’s Juno probe at Jupiter-orbit after a five year flight. Of course, for us Google Earth fans, the first question is ‘we have Google Earth, Google Moon and Google Mars, why not Google Jupiter?’.

The problem, however, with Google Jupiter, is that what we see of Jupiter, is actually its clouds and not its surface. The reason why Google Earth does not show the clouds by default and Google goes to great lengths to remove them from imagery is because they are ever changing, and the same applies to Jupiter’s clouds. There are some long term, large scale structures in the clouds such as the famous Great Red Spot, which is actually a large storm system, but even that changes shape and position over time. Even if we had the technology to see through the clouds, the surface is believed to be mostly liquid hydrogen and would again not have any static features. So, a Google Jupiter would serve almost no useful mapping purpose unless it included features not found in Google Earth.

The above observations apply to all the giant planets: Gas Giants Jupiter and Saturn, and Ice Giants Uranus and Neptune.

One possibility would be to have a Google Jupiter that kept updating, but for this we would need regular imagery of the planet. To get an idea of what this would look like, see the animation below. To see it in Google Earth, download the original animation it is based on found in this post created by Google Earth Community member ‘barnabu’ back in 2006.

Speed in milliseconds per image:

Of course there are other rocky planets like Mercury, which has been mapped, which would benefit from a Google Mercury, and there are many rocky moons, with Jupiter alone having 51 that are more than 10 km in diameter. There are some moons such as Saturn’s Titan which have such thick atmospheres that they will be difficult to map, but Titan does have surface features that almost certainly will be mapped eventually.


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Catégories: Sites Anglophones

Making use of the Google Earth API

mar 05-07-2016

Although the Google Earth API was officially deprecated back in December 2014, Google chose not to shut it down on schedule in December 2015 and have instead kept it running. Although you should not design websites around it as most browsers no-longer support the technology it is based on (NPAPI), it can still be a very useful tool for particular purposes.

Today we are sharing a tool based on the Google Earth API that we developed for our own purposes, but think others might find useful. If you have a collection of placemarks and want to know whether or not Google Earth has relevant imagery, rather than switching to ‘historical imagery’ then checking them one by one in Google Earth, this tool can do much of the work for you.

As an example of what it might be useful for, we took this page on Wikipedia that lists the locations of all the tornados in the United States from January to March 2016. We used the provided coordinates to create placemarks in Google Earth. We then used the tool to generate a new KML which shows which placemarks have imagery from 2016.

Placemarks with imagery from 2016 are highlighted in green.

It is not perfect in this particular situation as it does not tell us whether the imagery in question was captured before or after the particular tornado. To achieve that, we would have to find a way to include the tornado date in the original KML file. But it does considerably cut down on the number of placemarks we should check if we want to find signs of tornado damage. You can get the above KML file here. Keep in mind that many of the tornados were very week and did almost no damage.

In order to use this tool, you need to open this page in Firefox which, as of this writing, still supports the Google Earth API. The first time you open the page, you must click the link that says ‘Activate Google Earth’, then choose ‘Allow and Remember’ in the popup. Then refresh the page.

Next, select the KML file containing the placemarks you want to use, and enter a date in the space provided and click “Get Dates”. The tool will check the latest imagery date for each location and when complete will download a KML file which you can view in Google Earth. Note that each placemark takes two seconds because we find that the Google Earth API is a bit unreliable if rushed. The resulting KML file includes all placemarks from the original file and puts the date of the most recent imagery in the placemark description. In addition, it colour-codes the placemarks yellow and green depending on whether or not the latest imagery is before or after the date provided below.


KML file:

Date: (yyyy/mm/dd)

Get Dates

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Catégories: Sites Anglophones