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Ships in Google Earth’s 3D imagery

ven 16-09-2016

We have long had a fascination with cruise ships in Google Earth. Cruise ships represent a significant amount of area and deserve to be mapped, but because they move from place to place this poses an interesting mapping problem.

Some time back we had started making a collection of placemarks for various ships in 3D, but never got around to completing it. In some cases, the name of the vessel can be seen in the imagery, but at other times, identifying it requires a bit of detective work. Yesterday, GEB reader Frank (not Frank Taylor), who also contributes outlines for our 3D imagery KML, sent us a collection of placemarks for cruise ships and ferries in 3D, which was much more comprehensive than ours. So, we have combined it with our collection and are providing it here in case our readers are interested.

Grab the collection here. We have implemented it as a network link and may update it over time, but do not guarantee that we will have time to do a lot of regular maintenance.

Remember that Google Earth does not have a ‘historical 3D’ feature, so when 3D imagery gets updated, the placemarks will no longer be correct. We found that a few of the placemarks we had created in the past no-longer have cruise ships at those locations. Also interesting is that some of the cruise ships in the imagery have since been sold and renamed or in some cases, such as the Sky Wonder, have since been scrapped.

A number of cruise ships appear more than once in the 3D imagery. For example:

Norwegian Pearl, in Seattle (west coast of the US).

Norwegian Pearl, in Miami (east coast of the US).

Crystal Serenity in Barcelona, Spain.

Crystal Serenity in Livorno, Italy.

If you are interested in collections of ships visible in 2D imagery, be sure to check out the Google Earth Community transport collections.

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Catégories: Sites Anglophones

WorldView-4 and SkySat launches

jeu 15-09-2016

The next couple of days will see two significant launches for satellite imaging. If all goes well, the first launch will be four SkySat satellites owned by Google’s Terra Bella. They are expected to launch with a Peruvian reconnaissance satellite aboard a European Vega rocket. See the count down clock and more launch details here. The launch takes place from ZLV, Kourou, French Guiana. Next will be WorldView-4. It is a DigitalGlobe satellite with similar specifications to WorldView-3, the current leader in high resolution commercial satellite imagery. According to the launch is scheduled to take place from SLC-3E, Vandenberg Air Force Base, California.

[ Update: We didn’t realise at the time of writing that PerúSAT-1 which is being launched together with the SkySat satellites is also an Earth observation satellite with a resolution of 70 cm per pixel panchromatic and 2 m per pixel for colour. Learn more about it here]

We already looked at WorldView-4 last month, so today we are focusing on the SkySat satellites. Those being launched today are SkySats 4 through 7. SkySat 1 was launched in November 2013, SkySat-2 in July 2014 and SkySat-3 in June 2016. We have seen imagery from them a number of times, including imagery of the damage caused by Italy’s recent earthquake, a poppy display at the Tower of London and the Burning Man festival. We also once came across a SkySat image in the Sahara, which has since disappeared from Google Earth.

The SkySat satellites have an imagery resolution of about 90 cm per pixel. This is not as good as WorldView-4’s 30 cm per pixel, but is better than Planet Lab’s Dove satellites, which have a resolution of 3-5 m per pixel. It is also better resolution than the best imagery Google Earth currently has for some locations, so we hope Google considers using Terra Bella imagery to fill in the gaps in Google Earth.

Unlike SkySats 1 and 2, SkySat-3 has propulsion, which gives it greater flexibility in capturing images. Presumably 4 thorough 7 also have propulsion. Read more about the differences between SkySat’s 1 and 2 and SkySat-3 here.

The SkySat satellites being prepared for launch as tweeted by CNES. Image credit ESA-CNES-ARIANESPACE.

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Catégories: Sites Anglophones

Cloudy places are hard to photograph from space

mer 14-09-2016

In yesterday’s post we talked about Landsat imagery and how in some locations there are only a few cloud-free images per year. That was not entirely accurate, as there are some places where it is practically impossible to find a cloud-free Landsat image.

We were recently exploring the north-western area of Colombia (looking for circular islands) and noticed that the imagery there is of particularly poor quality. Most of Choco Province, Colombia, has no high resolution satellite imagery and instead uses the global mosaic created from Landsat imagery. To make the global mosaic, Google took Landsat imagery captured over multiple years and searched for cloud-free pixels to use in the final mosaic. However, when we looked at the region with our Landsat animations KML we found 49 images captured over the last three years, but they all had significant cloud cover. The very best image is listed as having 17% cloud cover and the next best has 31% cloud cover and the rest are much worse. Add to this the fact that the clouds tend to form around particular geographical features and there are some places that never have cloud free pixels. The result is particularly poor imagery in some places.

Some areas look like abstract art.

We are not sure whether the above effect is due to just cloud cover, seasonal changes in the water levels or the fact that the water colour changes over the seasons.

For an understanding of which parts of the globe have the most cloud cover, see this animation from NASA. Note that it is not images of clouds, but rather maps of the average amount of cloud cover over a month. We could not find a version for longer periods to find out which locations have near-permanent cloud cover.

Another place with a similar problem – year round cloud cover – is the rainforest belt of Central Africa. In some places, Google has had to use Landsat 7 imagery, which we can see because of its characteristic stripes due to a faulty component on the Landsat 7 satellite.

Landsat 7 stripes.

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Catégories: Sites Anglophones

Land lost vs. land gained

mar 13-09-2016

We recently came across this interesting article by National Geographic about a recent study of land/water changes over the last 30 years. The study is by researchers at the Deltares Research Institute, who used Google Earth Engine to gather and process the data. The data itself comes from Landsat imagery.

Sadly, we were not able to figure out a way to view the data in Google Earth. Google Maps Engine (not to be confused with Google Earth Engine), which was shut down earlier this year, was notable for being able to easily display maps in Google Earth. However, it appears Google Earth Engine does not have any such features and is mostly focused on displaying data in Google Maps style 2D tiles. This is a pity, because we find Google Earth a much better platform for exploring this kind of data.

The researchers published the full description and analysis of the project in the journal Nature Climate Change, which is subscription based. However, the data itself is published as a publicly available 2D map.

What we were not able to determine were the details of how the water bodies were measured. Most inland water bodies are quite seasonal, so we wonder how the researchers corrected for that. The Landsat imagery typically covers each spot on earth once every 16 days, but a fairly high percentage of the images are obscured by cloud. This often means only a few good images per year. For the global mosaic used in Google Earth, many different images over multiple years are combined together to get the cloud-free image. However, this can result in some weird effects where water is concerned, because water is not just seasonable, but can vary considerably from year to year (more on this in a future post). Droughts or floods can, for example, be one-in-a-hundred-years events. This doesn’t apply to coastal land reclamation, which tends to be permanent.

Land reclamation near Seoul, South Korea, as seen in Aqua Monitor.

We have previously created animations of land reclamation and artificial islands being built, but we restricted ourselves to the time-frame visible in Google Earth historical imagery, rather than the 30 years used in the above study.

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Catégories: Sites Anglophones

Remembering 9/11 with Google Earth

lun 12-09-2016

We have done a number of posts in past years covering various aspects of the 9/11 terrorist attacks and the subsequent rebuilding of the World Trade Center, so today we will only be looking at some of the most recent changes.

If you go to the site of the World Trade Center in Google Earth, and turn off the 3D buildings layer, the whole square is sunk into the ground by about 20 m. We believe this was either done when it was a construction zone (and was below ground level), or, it is to stop the satellite imagery showing through when 3D imagery is turned on since the two pools do sink into the ground.

The satellite image currently in the default layer was captured on June 26th, 2016, and shows 3 World Trade Center nearing completion. According to Wikipedia, the concrete core is now at its maximum height. It can also be seen in Street View. We previously created a Street View slideshow showing the changes that have taken place over the years. We have updated it with the most recent imagery below.

Speed in milliseconds per image:

We believe there are at least two major buildings still to be built at the site. World Trade Center 2 in the corner opposite the pools, and World Trade Center 5 on an adjacent block. See Wikipedia for more.


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Catégories: Sites Anglophones

Weird altitude effects in Google Earth

ven 09-09-2016

Yesterday we made some Google Earth tours of various US parks. We recorded the tours using Google Earth’s built in ‘Record a tour’ button on the tool bar and then navigating with a SpaceNavigator 3D mouse. Everything seemed fine until we played back the tours and found that some of them have bumps in them and occasionally some have quite severe up and down jitter. We found that these effects were actually part of the tours as they would occur in the same place when played again.

Our first thought was that not all the terrain had loaded properly when the tour was recorded. Thus Google Earth recorded the wrong altitudes when recording the tour, and when playing it back new altitude data is available, so it looks wrong. After much investigation, we do believe that is the main cause of the problem, but that there are other issues as well.

We thought it would be interesting to try and fix the tours by using some maths to smooth out the altitudes. However, we found that because of the way the altitudes are stored in the tours, smoothing them out may be difficult or impossible. Google Earth can store altitudes in two basic ways: relative to the ground (or sea floor) or an absolute measurement (from sea level). A third option is to leave out the altitudes and have objects automatically clamp to the ground level. The difference between relative altitudes and absolute altitudes is not always obvious, but in some cases it is important to get it right. We had a problem with this in the past when we created a script to draw arcs. If we used absolute altitudes the ends of the arcs ended up all at a fixed altitude instead of ground level, and if we used relative altitudes then the arcs were not smooth but included all the bumps from the ground below them. The eventual solution that we came up with was to use absolute altitudes and read the end point altitudes from Google’s Elevation API.

In the case of the tours, we found that they are being stored as relative altitudes, which is a problem, because we cannot smooth them out without knowing what the ground altitude is at each point. Oddly enough, this contradicts what it says on this page, which states that Google Earth uses absolute altitudes for tours precisely because of the problems we are experiencing.

As we noted in this post, when viewing areas with 3D, Google Earth shows the altitudes from the 3D imagery in the status bar. So the first thing we wanted to do was determine whether or not relative altitudes in Google Earth are treated as relative to the default terrain at all times, or whether it uses the 3D imagery where available. What we found was surprising.

Google Earth does use the default terrain at all times, but in some cases, it modifies the default terrain when you turn on 3D imagery – and this modification includes the altitudes being used in the relative altitude calculation. Typically, 3D imagery is above the default terrain, so intuitively one would expect relative altitudes to move upwards when you turn on 3D imagery. What happens is the opposite. They move downwards. This is because Google Earth adjusts the default terrain downwards to stop it from being seen in the 3D imagery.

The two scenes below illustrate what happens. In both cases we have some lines set to a fixed height relative to the ground:

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Left: ‘3D buildings’ layer turned on. Right: ‘3D buildings’ layer turned off.

Left: ‘3D buildings’ layer turned on. Right: ‘3D buildings’ layer turned off.

In the second scene above, we are looking at a corner of an area with 3D imagery and you can see how the area with 3D imagery actually has a lower ground level than the surrounding areas. An extreme case of such altitude adjustments can be seen in this post.

In addition to all this we encountered one or two places where Google Earth couldn’t decide what the correct altitude was. If we had a line with a relative altitude set, it would jump between two different heights, depending on which direction we looked at it. We believe this is the cause of the worst of the jitter we experienced in the tours.

We still don’t have an actual solution to our problem.

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Catégories: Sites Anglophones

US National Parks in 3D for centenary

jeu 08-09-2016

In our August month-end post we mentioned that Monument Valley, Arizona / Utah was now in 3D in Google Earth. Several other US parks also received 3D imagery at the same time. What we didn’t realise at the time was the reason why Google added 3D for several US parks at the end of August. It was because the US National Park Service celebrated its hundredth birthday on August 25, 2016.

So, to show off the 3D imagery available for US Parks, including both the new releases and areas that already had 3D (which we looked at last October), we have created some Google Earth tours, which you can view in Google Earth with this KML file. We have also recorded a select few of them in the YouTube video below:

Note that we haven’t created tours for every US park that has 3D. We have included outlines for the parks we know about that have 3D. If you notice we have missed any, please let us know in the comments.

For a map of all areas, not just US parks, that have 3D use this KML file.

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Catégories: Sites Anglophones

Italy’s earthquake

mer 07-09-2016

As of this writing, Google has not updated ‘historical imagery’ for almost three months. Up until mid-June they had been updating it almost weekly. As a result, there have been a lot of events over the past few months that we know were captured by DigitalGlobe but we cannot access the imagery in Google Earth. One such event was the deadly earthquake that struck central Italy on August 24th, 2016.

Google has provided an image of the region from one of Terra Bella’s SkySat satellites. It can be viewed in Google Earth using this KML file. They SkySat satellites are not as high resolution as most Google Earth satellite imagery, but in this case, some of the affected regions in Italy do not have high resolution satellite imagery – all they have is SPOT imagery, which is lower resolution than the SkySat imagery. Despite the relatively low resolution, we can see some of the effects of the earthquake in the imagery.
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Before image: CNES/Spot Image. After image: Google / Terra Bella.
Amatrice, Italy. 1: The location of most of the damaged buildings. 2 & 3: Tents set up after the disaster.

We also saw tents in Grisciano in the Terra Bella image.

Also worth looking at is the Copernicus Emergency Management Service. They gathered satellite imagery of the affected region almost immediately after the earthquake and within a day or two had arranged aerial imagery too. The results can be seen on this page. There are maps of the affected towns, including grading the amount of damage down to building level. For example, you can see the map for Amatrice in this PDF.

Sentinel-1A and Sentinel-1B are radar satellites that are particularly good at detecting changes in terrain. You can see deformation maps in this article that uses images from before and after the event to detect how much the ground had moved after the earthquake.

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Catégories: Sites Anglophones

Were US spy satellites from the 70’s better resolution than Google Earth?

mar 06-09-2016

We recently came across this story in which a former engineer on the US spy satellites known as Keyhole-9, claims that the imagery they got from those satellites were ‘better than Google Earth’. We thought this was something worth looking in to.

Google Earth has imagery in a wide range of resolutions from a few centimetres per pixel to more than 15m per pixel. The highest resolution photos are taken from very close to the ground, such as some photos of the island of Manihi that Frank captured with a camera attached to a kite. Next highest is aerial imagery that is captured from aircraft and covers the continental US, much of Europe, Japan and a number of other locations around the world. Aerial imagery in general is better resolution than can reasonably be captured by satellite. So, for a fair comparison we need to look at satellite imagery only.

All references we have been able to find say that the best resolution that the KH-9 satellites provided was 2 to 4 feet. That translates to about 70 cm per pixel at best. If you look at this list you will see that many commercial satellites that provide imagery to Google Earth actually do a bit better than that, the best being DigitalGlobe’s WorldView-3 that can manage 31cm per pixel. WorldView-4, due to be launched this month will have similar resolution. So, although the resolutions achieved by the KH-9 satellites are certainly impressive, they were not actually better than Google Earth provides today.

The maximum possible resolution of a camera depends on the size of the collector (typically a mirror) and the distance from the target. The KH-9 satellites were in non-circular orbits with lowest altitude of about 150 km. Most modern commercial imaging satellites are in higher orbits anywhere from 400 – 800 km up. The lower orbit of the KH-9 satellites gave them a resolution advantage, but it may also be the reason why they were only in orbit for around 3 to 9 months each. The best imaging satellites today probably achieve their greater resolution despite the higher orbit through a combination of larger mirrors and better quality optics, although we could not find any actual data on the size of the mirror used in WorldView-3. The KH-9 satellites had 0.91 m diameter mirrors.

In the past it was actually illegal in the US to sell imagery with better than 50cm per pixel resolution, but in June 2014 DigitalGlobe was given permission to sell higher resolution imagery – up to 25cm per pixel.

One of the photos shown in the clip in the CNN story and also shown on Daily Mail’s version of the story, which shows an overhead shot of people having a picknick is clearly higher resolution than 2 feet per pixel and we believe is not a satellite image. Another of the photos they show is of a submarine at the Russian naval yard at Severodvinsk, which we discussed in this post. At the time we noted that it was a remarkably good image for its age, although not quite as good as the Google Earth imagery.

A declassified photo of a submarine (cropped for better resolution) in Severodvinsk, Russia. The image was captured in October 1982 by KH9-17. Full image here.

The same location (and probably the same submarine) as seen in Google Earth.

It is no coincidence that the company Google Earth originally came from was named Keyhole Inc. It was in direct reference to the Keyhole spy satellites. To this day, Google Earth saves files in the KML format which stands for Keyhole Markup Language.

The Corona program (that flew the Keyhole satellites) and most of the imagery from them has been declassified. The imagery can be obtained from the USGS. Some has been digitized and is available to download for free via Earth Explorer (look for the ‘Declassified data’ data sets). For imagery that hasn’t been digitised, for a fee of US$ 30 per scene you can have them scanned. The imagery is not just of Russia and China. In fact the only high resolution imagery we were able to find was of the US and Antarctica.

You can read more about the KH-9 satellites on Wikipedia.

A documentary about the start of the Corona program can be found on YouTube.

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Catégories: Sites Anglophones

Higher resolution contours with Mapzen altitude tiles

lun 05-09-2016

We recently did several posts about drawing contours using altitude data from different Altitude API services. The first version used the Google Maps Elevation API and the second used Mapzen’s elevation API. The biggest problem was that the API’s are throttled, in Google’s case, to stop abuse and in Mapzen’s case due to limited server resources. Mapzen recommended that we instead switch to using a different method for getting the altitude data from them. They provide the option to get the altitude data in the form of tiles similar to the tiles used in various online maps, but with just altitude data. So, we decided to try it out.

We expected some improvement in speed, but it turned out to be a much faster method than we expected and works very well. The tiles are 256 x 256 points of data and we use one to four tiles, depending in the location chosen. The previous method using the elevation API took about 40 seconds to retrieve 100 x 100 data points, but the new method gets all the data in a couple of seconds despite getting many more data points. In fact we found it was so fast and because it was allowing us to get so much more data, what was taking the time was calculating the contours. So we did some work on optimising that code and managed to get that quite a bit faster too. It can still take a little time if you ask for too many contours close together.

We tested it on Rio de Janeiro and discovered that Mapzen’s elevation data is better than Google Earth’s default layer for Rio. We discussed in this post the poor quality of Google Earth’s elevation data for Rio by comparing it to the 3D data. Mapzen assures us that all their elevation data is open data, so Google might want to look into the discrepancies and update their data. There are probably other areas where Google Earth’s data is better quality, as Google has access to a number of different data sources for elevation data, some of which are not in the public domain.

In Google Earth with 3D imagery turned off, Sugarloaf Mountain is entirely at sea level, but the Mapzen data results in reasonably accurate contours of it.

The faster data access allows us to draw more detailed contours than before.

Note that we automatically zoom in or out depending on the size of the area you choose. We could, if if we choose produce much higher resolution contours in most instances although it would take a lot longer to calculate. We chose not to as this is just a ‘for fun’ project that we haven’t really found a use for. If any of our readers has a specific use for it and does require higher resolution contours, then let us know in the comments and we will consider adding that option.

Using it is similar to last time. Just draw a polygon of the area you are interested in, save it as a KML, upload it here, select the desired options then click ‘Draw contour’.

input,select{ padding:5px; margin-bottom:2px; color:black; }

Create KML
Curves (experimental)
Mode: Single contourMultiple contours
Altitude: m above sea level
Contour every: m
Draw Contour Cancel

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Catégories: Sites Anglophones

‘The Eye’, a rotating island in Argentina

ven 02-09-2016

We recently came across this article about a floating island in Argentina that rotates. Producer and film director Sergio Neuspiller discovered it when filming in the area and has since started a Kickstarter to raise funds to investigate it further. See the Kickstarter promotion video below:

The island is visible in Google Earth imagery and has been in existence since at least 2003, the date of the oldest Google Earth image of the location. Here is an animation showing how it moves over time:

It is fairly obvious what is happening (no, it’s not an alien base as some have suggested). When you have a floating island and a water current that flows along one side of it, it will naturally rotate and become circular over time, as well as carving out a circular hole. The phenomenon is quite rare, because the conditions must be just right. Floating islands of plants are themselves quite rare, but in addition, it requires a current, though a fairly slow moving one.

[ Update: We believe wind may be the main factor in some instances rather than current. ]

There is a special type of floating island that is very common and that is ice. The phenomenon does occur with ice, as you can see in the YouTube videos below:

We tried to find other examples of rotating floating islands not made of ice and we found one on a lake in India:

Read more about it here.

We also found a reference to one in the Okavango delta. You can read the full story about it in a PDF found here. Apparently a Brian Wilson discovered a rotating floating island and identified it in aerial imagery from as far back as 1944. It could be seen to have kept rotating up until about 1974, when it attached itself to one side of the lagoon it was in and remained there until at least 1990. We had a look at the coordinates given and not far from that location did indeed find a floating island that has moved between 2006 and 2016. We cannot positively confirm that it is the same island.

But for the real treasure trove of rotating floating islands, the place to go is the Luapula River on the border of Zambia and the Democratic Republic of the Congo (DRC). Sadly, there isn’t a lot of historical imagery, so good animations were not possible. So, we are showing them in the form of ‘before and afters’ to demonstrate that the islands do, in fact, move.

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A round one, an oval and another shape, sharing a pool.

If we are not mistaken, the dark patches are fire scars, suggesting the island can sustain fires without destroying it.

A whole bunch of floating islands!

And that’s just some of them. There are many more! Amazingly, we could just (although only just) see some of them moving using our Landsat animations KML file.

To see the above locations in Google Earth, including historical imagery tours, download this KML file.

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Catégories: Sites Anglophones

Google Earth Update – Version:

jeu 01-09-2016

Google has released an update to Google Earth and Google Earth Pro. This is a bugfix / maintenance update with no new features. Having said that, it does fix some of the worst bugs, including crashes and installation problems and its release confirms that Google has not given up on Google Earth.

The release notes say:

Issues fixed in this release of Google Earth Client

  • Removed menu items for Google Maps Engine and the Google Earth Community.
  • New Google and Google Earth logos.
  • Crashes from rearranging items in My Places.
  • Earth Pro: Removed registration dialog as Pro no longer requires a license.
  • Windows: “1603” installer error caused by attempts to re-install Earth 7.
  • Linux: Font dialog and other crashes.
  • Linux: Cache data inconsistency between 32 and 64-bit builds.
  • Linux: RPM installer problems with permissions in directory “/usr/bin”.
  • Mac & Linux: Updated driver support for 3Dconnexion controller devices.

We get quite a lot of email regarding problems installing Google Earth, so we hope those are now a thing of the past. We also used to experience the crash when rearranging placemarks, which was particularly annoying as it typically meant redoing a lot of work, as My Places is only saved when Google Earth is closed properly. It seemed to be placemarks with links that were particularly troublesome.

Another crash that occurs when you try to search for something while Google Earth is still loading is still there.

For most people, there should be no need to do anything and Google Earth should update automatically. If you installed Google Earth using the ‘offline installer’ then you may need to manually update to get the new version, which you can do by going to the standard Google Earth download page. We actually recommend that everyone consider switching to Google Earth Pro as it is free and has more features than Google Earth. There are, however, no differences in the actually imagery. Google Earth and Google Earth Pro share the same ‘myplaces.kml’ (where saved placemarks are stored) so switching does not loose any data. You can have them both installed at the same time and use whichever you like, but you cannot run them both at once. As mentioned in the comments, Google Earth Pro is not available on Linux.

Old icon New Icon New Logo

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Catégories: Sites Anglophones

The best of Google Earth for August 2016

mer 31-08-2016

Google has not updated ‘historical imagery’ for nearly two months, so we have not been able to monitor imagery updates. Just yesterday, a number of new 3D areas were discovered and reported in the comments of this post. We haven’t yet had time to look through them all, but Monument Valley, Arizona / Utah is fantastic. Other than yesterday’s finds, there haven’t been many significant 3D imagery releases in August except for Rio de Janerio, which got an update to the key Olympics locations. We also had a look at Washington D.C. 3D imagery, which was released at the end of July, but it appears to have been removed again. That is probably only temporary.

Monument Valley, Arizona / Utah.

The only major Street View addition this month was a significant expansion to the coverage in Indonesia, including Komodo Island, home to Komodo dragons.

We had a look at the major flooding that struck Louisiana earlier this month. Although Sentinel imagery is relatively low resolution (10 m per pixel) we could clearly see large neighbourhoods had severe floods.

We did a series of posts on the Sentinel and Landsat imagery available on Amazon Web Services (AWS). We created KML files to allow you to see thumbnails of the most recent images as well as animations automatically created from the thumbnails. In addition, we had a look at the coverage of the two sets of imagery, both by most recent image and quantity of images.

We did a post on how to simulate lakes in Google Earth and GEB reader ‘DJ’ suggested creating a contour line draw-er, so we did.

We had a look at an animation of the Maokong Gondola, a cable car system in Taipei. The animation was created by Steven Ho.

We had a look at various landslide dams around the world. These are dams that are formed when a landslide blocks a river, forming a dam with a lake behind it, often with catastrophic consequences if the dam collapses.

We had a look at the India-Bangladesh enclaves and wondered whether we should try to update Google Maps to reflect the fact that India and Bangladesh swapped most of the enclaves so they no-longer exist.

We had a go at seeing Antarctic seals in the Google Earth imagery and believe we were successful.

We had a look at DigtialGlobe’s new satellite WorldView-4, that is expected to be launched mid-September. It is similar in capabilities to WorldView-3, so don’t expect improvements in imagery resolution, but rather a greater quantity of good imagery, as DigitalGlobe will have more opportunities to photograph a particular location. Whether this will have a noticeable effect on what we see in Google Earth remains to be seen.

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Catégories: Sites Anglophones

‘Stabilizing’ our Landsat imagery animations

mar 30-08-2016

We have recently been doing a series of posts about Sentinel and Landsat imagery on Amazon Web Services (AWS), including releasing a KML file that automatically retrieves thumbnails of Landsat 8 imagery from AWS and creates animations with them.

We mentioned at the time that the Landsat images are not all perfectly aligned with each other and we had adjusted each image slightly to try and create smoother animations. To do this we used a simplified model that assumed that the imagery squares were all aligned with latitude and longitude, with up being North. It turns out that our assumptions were not valid and there was still significant ‘shake’ visible in ground features in many animations, especially those of Antarctica.

After some investigation we discovered that not only do the Landsat tiles tilt to the right as per the satellite’s orbit, but the images are placed into the thumbnails at an angle, results in further rotations overall.

The imagery on AWS is provided with a file whose name ends with MTL that contains a variety of metadata for the image. This includes the coordinates of the corners of the thumbnails (this is the whole thumbnail including the black areas). If we have two thumbnails offset from each other as shown as the red and green squares in the image below left, then in our animation we need to adjust the top of one of the images by the amount shown as the ‘top offset’. Thinking of it as purely Cartesian coordinates, to work it out in latitude and longitude it involves a series of rotations and translations, which gets rather complicated. However, we realised that instead, we could stick to proper geographic calculations, for which we already have the key routines that we worked out when working on our post on drawing circles in Google Earth. It mostly relies on an open source package called GeographicLib by Charles Karney, with a few additions also by him but not included in the main library.

Above right we see the mid-point (red circle) of the top of the red square, the mid-point (green circle) of the top of the green square, and what is known as the ‘cross track intercept’ for the green point to the thin red line. The cross track intercept is the closest point on a great circle to a location not on the circle. The distance we were looking for is from the red circle to the cross track intercept. Although this all sounds complicated, it is actually only a few lines of code, because all the hard work is done by GeographicLib. We simply repeated this for all for sides and for every frame in the animation and it worked! The animations are now much more stable even over Antarctica.

Our conclusion overall, is that although geographic coordinates can be very complicated, sometimes it is actually easier to work with them than trying to simplify things, as the heavy lifting can be done by ready-made libraries of code.

Because the thumbnails are actually higher resolution than what you can see in the popup, we have also added the ability to zoom and pan the animation. Just use the mouse scroll wheel to zoom in and out and drag the image with the mouse to pan. I am afraid we don’t have a Mac to test on, so we are not sure if this works on Mac. Let us know in the comments if it doesn’t and we will try adding keyboard controls. Remember, these are only thumbnails so don’t expect great resolution when zoomed in.

You can download the updated KML file here. We have widened the size of the popup in the standard version, but if you find it too wide for your screen, then the narrower version can be found here.

The post ‘Stabilizing’ our Landsat imagery animations appeared first on Google Earth Blog.

Catégories: Sites Anglophones

India–Bangladesh enclaves

lun 29-08-2016

We recently came across this humorous take on the complicated border between India and Bangladesh. The video mentions that India and Bangladesh had agreed to swap enclaves in 2015 in an effort to simplify the situation. Wikipedia says the same and states that the agreement was ratified on June 6th 2015 and that the physical exchange of enclaves would be implemented in phases between 31 July 2015 and 30 June 2016. So, we immediately had a look in Google Earth, but found all the enclaves still displayed. Google Earth gets its map data, such as roads and borders from Google Maps, but it can often take some time for changes to get to Google Earth. However, in this case Google Maps also shows all the enclaves.

India–Bangladesh border – Google Maps.

So, we checked various other mapping services and found that:
– MapQuest and Open Street Map show the same borders as each other and we believe they share the same data. They do not show all the enclaves that Google Maps does, but do appear to show two large enclaves named Dahagram and Jote Nijjama (names from Google Maps).
– Bing Maps and Here appear to have identical border data and show no enclaves at all. In addition the borders do not exactly match the other services. They are generally lower resolution but not all the differences can be easily attributed to this.

India–Bangladesh border – Bing Maps.

India–Bangladesh border – Here.

India–Bangladesh border – Map Quest.

India–Bangladesh border – Open Street Map.

So which are correct, and where does one get the official border data from? It must be noted that the enclaves along the India-Bangladesh border were not disputed borders, just very complicated ones (prior to the enclave swap). If the border was disputed then it would get even more complicated as there would be at least two ‘official’ versions of the border. In fact, India recently considered enacting a law to control how maps of India, including its borders are shown, with possible fines of up to 15 million dollars for violators.

Do any of our readers know whether all or some of the enclaves no-longer officially exist? It would appear the border can be edited in Google Map Maker, so we could fairly easily get the enclaves removed from Google Maps (and hence Google Earth) if we can find reliable information about which ones no-longer exist.

Another location with complicated borders is Baarle-Hertog, a municipality of Belgium, which consists of 24 separate exclaves inside the Netherlands. Baarle-Hertog has embraced the situation and made it into something of a tourist attraction.

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Animating Sentinel-2 imagery in Google Earth

ven 26-08-2016

We have recently been doing a series of posts about Sentinel and Landsat imagery on Amazon Web Services (AWS). We created tools to let you quickly preview the latest Sentinel and Landsat imagery in Google Earth. We also looked at the coverage pattern for the Sentinel and Landsat imagery and created a way to animate the Landsat imagery. Today we are releasing similar animations for the Sentinel imagery.

To see the animations, simply download this KML file, open it in Google Earth and click on any tile.

There are several differences between this one and the Landsat animations. The sentinel thumbnails are much lower resolution than the Landsat thumbnails, so we don’t provide a link to a larger version. Also, the sentinel images often do not cover the complete tile, so we have provided an extra slider to allow you to filter out tiles based on how much of the tile they cover.

The KML file also shows with colour coding how much sentinel imagery there is, with a range from green to red for 1 to 120 images per tile and white for tiles that have over 120 images. The highest numbers can be found over Europe, which is understandable given that it is a European satellite. The amount of imagery also increases towards the north of Europe, we believe this is because the paths the satellite takes overlap more near the poles, allowing more imagery to be captured. There are also hotspots over deserts suggesting that the images are selected for low cloud cover.

The Sentinel-2A satellite that is gathering the imagery was launched in June 2015. In comparison, Landsat 8 has been around since 2013. However, the Sentinel-2A satellite covers the globe roughly every 10 days, whereas Landsat 8 takes 16 days. In addition, the Landsat 8 archive on AWS only includes selected images from 2013 and 2014 (with significantly more of the US than other parts of the world) and only has the complete set of images from 2015 onwards.

We also find the clouds look whiter and obstruct the picture more in the Sentinel imagery than they do in the Landsat imagery. This may relate to how the imagery was processed for the thumbnail or it could reflect differences in the exact wavelengths the respective satellites use to capture the colour bands.

We came across a few errors in the data, such as mislabelled tiles or missing thumbnails, but they were not significant enough to seriously affect the operation of the animations.

As with the Landsat imagery, it is important to note that this is very low resolution imagery, so expect to only see very large scale phenomena. Also, with only a year’s worth of data there is not a lot of change to see. However, it is a continuously updated service and with the expected launch of Sentinel-2B sometime next year doubling the frequency of imagery, we can expect some spectacular animations in years to come.

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Catégories: Sites Anglophones

Improving our contour algorithm

jeu 25-08-2016

Last week we featured a tool for drawing contours which uses the Mapzen altitude API. We used a very simple algorithm called ‘Marching Squares’ that we found on Wikipedia. However, as GEB reader Боби Димитров pointed out in the comments, if you try to use too low a resolution altitude grid relative to the number of contours you want, you end up with something looking like abstract art:

The Marching Squares algorithm is remarkably simple. We just check every altitude from the grid obtained from the Mapzen API and colour it red or green based on whether it is above or below the altitude of the contour we wish to draw. Then we draw lines separating the two colours from the mid-points of the rectangles in the grid as shown below:

However, we realised that if between a red and green dot rather than using the mid-point, we check the altitudes of the points relative to our contour altitude and then use a point proportionally closer to the point closest to our contour altitude, we end up with a much better result:

All the settings were the same as the ‘abstract art’ sample above, except we used proportional ‘mid-points’ on our squares.

And best of all, it only required changing one line of code!

We have also tried smoothing out the contours using this open source code. It generally works well, but it tends to result in some unwanted loops, so we probably need to look for a different curve algorithm.

Our next step will be to use a different technique to access the elevation data from Mapzen, as suggested by them in the comments. If successful, it should allow much faster access to the elevation data – and thus higher resolutions will be possible.

The changes so far have been added as options in last week’s post.

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Catégories: Sites Anglophones

4D Gondola in Google Earth

mer 24-08-2016

Steven Ho, whose work we often cover has recently updated a Google Earth animation he first created back in 2007 showing the Maokong Gondola of Taipei. We covered his original 2007 version in this post.

Below is a YouTube video of the tour, but we highly recommend also trying out the KML tour, which you can download from Steven’s blog.

It is excellent work and shows off some of the capabilities of Google Earth tours. It also highlights a few of the limitations. For example, it is not possible to stop and look around without pausing the animation, so you can only really see the animation from the angles provided in the tour.

A lot of work clearly went into getting it all right. There are 147 cable cars all moving correctly along their cables, which follow a long twisting route. He also notes that he does some tricks with the satellite imagery, switching between the default view and ‘historical imagery’. He does this because the default view shows a more uniform view from high altitudes, but actually has quite old imagery when you zoom in. Google has kept imagery from 2006 in the default layer because it is better quality than more recent imagery. However, the Maokong Gondola was opened in 2007, so for the closeup section of the tour, Steven switches to the more current imagery (from February 2016) found in ‘historical imagery’.

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Catégories: Sites Anglophones

Animating Landsat imagery in Google Earth

mar 23-08-2016

[ Update: We have updated the KML file with more stable animations and the ability to zoom in and out and pan. See this post for more details. ]

Last week we created a KML file to easily preview the latest Landsat imagery. The data comes from Amazon Web Services (AWS), which hosts a large amount of Landsat 8 imagery and also includes some handy thumbnail images. So, we have now created a KML file that automatically creates animations from the thumbnail images.

Yesterday we had a look at the coverage of Landsat 8 imagery, but we focused on how recent the imagery is. Today’s KML file instead colour codes tiles based on how many images are available. As we noted yesterday, the AWS archive is Landsat 8 imagery only and does not include all the imagery. It turns out that most of the world includes a fairly comprehensive set of imagery from 2015 onward, whereas the US (excluding Alaska) has imagery going back to 2013. The result is that most locations outside the US have about 44 scenes whereas the US typically has nearly twice as many, at about 77 scenes. Note that these figures increase with time as the data is live and a new scene is added to each tile approximately every 16 days.

To see the animations, download this KML file. Click on any coloured tile for an animation of that location. Depending on your internet speed, it may take a short while to load all the images. If you have slow internet or just want to get a quick preview of the animation, then download this KML file which uses smaller, much lower resolution thumbnails.

There are three sliders. The first slider shows the progress of the animation and allows you to manually switch between images. Doing so stops the animation, which you can restart with a button. The second slider lets you adjust the speed of the animation. The third slider allows you to filter the images by removing any with over a specified level of cloud cover.

AWS provides two thumbnails for each scene, one quite small one that we used for our ‘recent images’ KML and one much larger. We have used the larger one even though it is too large to comfortably show at full resolution in Google Earth. To see the animation running at full size, click the link at the top right of the animation window.

Remember that Landsat imagery is very low resolution (about 30m per pixel) and these are just thumbnails, which are even lower resolution than that. So expect to only see very large scale changes. Look for changes in seasons, ice cover changes near the poles, lakes shrinking or growing, and even sand dunes moving.

A major problem is the large amount of cloud cover in the images. If you select only scenes with minimal cloud cover you end up with only a few images to work with. This highlights just how difficult it is for commercial providers to get good imagery.

One problem we had was that the images are not captured from exactly the same angle, so there was a significant amount of shaking in the animations. We have tried to fix this by reading the latitude and longitude metadata included with the scenes and moving the individual images appropriately. It is still not perfect as a browser cannot position an image at sub-pixel resolution.

Let us know in the comments if you find anything particularly interesting. Ideally tell us the path and row.

Here is an interesting tile in Chad that shows some fire scars.

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Catégories: Sites Anglophones

Landsat imagery coverage

lun 22-08-2016

Last week we looked at Sentinel imagery coverage and today we are doing the same for Landsat imagery.

First, note that we are discussing Landsat 8 imagery only, as Landsat 7 imagery is not currently available on Amazon Web Services (AWS), which is the source of our information. In addition, we have excluded Landsat imagery captured at night as we do not have the appropriate Row/Path data for night time imagery – although the imagery itself is available on AWS. Also, this page says that although all scenes from 2015 onwards are available, only a selection of cloud-free scenes from 2013 and 2014 are included.

You can see the colour coded coverage map in Google Earth with this KML file. Click on any square to see the date the last image was captured and how old that image is in days. Note that this KML is not dynamic and is based on the data as it was on AWS on August 20th, 2016.

For imagery captured in the last 20 days, we have used this colour range: with red for the most recent imagery and green as the oldest. Anything older than 20 days we have coloured white.

Having a look at the order in which strips are captured, we get a pattern like this:
1   10   3   12   5   14   7   16   9   2   11   4   13   6   15   8
which then repeats after the 16th day. See this post to see an animation of the Landsat orbit.

The imagery of Antarctica is mostly several months old, because it is too dark to capture good imagery over the winter months. There is also a horizontal stripe in the Atlantic and Pacific from February/March 2015 and a vertical stripe from May 2015. We do not know why they were captured, or why the oceans in general have never been captured.

Also of note is that some scenes are recorded with a slightly different scene identifier. Most of the scene identifiers start with the letters LC8, whereas these images start with either LO8 or LT8. There are only a few scenes with these different identifiers. According to this page this means that they include only data from the Operational Land Imager (OLI) or only from the Thermal Infrared Sensor (TIRS), whereas most Landsat imagery includes data from both sensors. We have included separate folders in the KML to show the coverage for the different cases. This page explains what each letter in a Landsat scene identifier means.

Our next project with this data is to create animations with the thumbnails.

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Catégories: Sites Anglophones