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‘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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Catégories: Sites Anglophones

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.

The post Animating Sentinel-2 imagery in Google Earth appeared first on Google Earth Blog.

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.

The post Animating Landsat imagery in Google Earth appeared first on Google Earth Blog.

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.

The post Landsat imagery coverage appeared first on Google Earth Blog.

Catégories: Sites Anglophones

How to quickly preview the latest Landsat imagery

ven 19-08-2016

Earlier this week we created a KML file to allow you to preview the latest Sentinel imagery. We used the fact that Sentinel imagery is available via Amazon Web Services (AWS). Given that Landsat 8 imagery is also available on AWS we thought it would make sense to do the same for Landsat 8 imagery.

Simply download this KML file, and click on any tile and it will show you the six most recent Landsat 8 images for that tile.

Clicking on the link below the image takes you to the relevant AWS download page. On the AWS download page, click the thumbnail to see a much larger thumbnail.

The post How to quickly preview the latest Landsat imagery appeared first on Google Earth Blog.

Catégories: Sites Anglophones

More fun with contours

jeu 18-08-2016

Last week we had a look at drawing contour lines using the Google Maps Elevation API. Because of restrictions on what you are allowed to do with Google’s elevation data, we chose not to create KML files from it. We have since talked to another elevation API provider, Mapzen and they assure us that they use open data from USGS and NOAA and we may do whatever we like with it.

Google’s elevation data, as used in Google Earth is higher resolution in many locations than the Mapzen data because Google also has access to the USGS and NOAA data, but have supplemented it with other sources that are not as open.

Here is an example of what is possible with the new version of our tool:

To try it out with default settings, just click the ‘Draw Contour’ button and see what happens. The default settings should take about 30 seconds to complete. When it is done, it shows the result in the Google Map below as well as downloading a KML file, so you can view the contours in Google Earth.

To use it with your own settings, draw a polygon in Google Earth of the approximate area you are interested in, save it as a KML then select it below. If you do not select a KML file then it will default to using an area in British Columbia, Canada that we selected for its very mountainous terrain.

You can choose how many rows and columns of altitude data to obtain. The Mapzen elevation API limits the number of queries you can make in a given time to prevent individual users from degrading the overall system performance, so very high resolutions take a long time. Remember that doubling the rows and columns will take four times as long to complete.

Selecting ‘Show Altitude Grid’ only works in conjunction with the ‘Single contour’ mode. It displays the grid of the altitudes that were obtained from the Mapzen API and shows them in red if they were above the selected altitude, or green if they were below the selected altitude. Zoom in to see how they relate to the contour line.

[ Update: Also see this post regarding improvements we made to the algorithm. ]

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

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

If you don’t like the styling of the contour lines in the KML, you can change them in Google Earth from the properties of the containing folder rather than having to modify each individual contour.

Let us know in the comments if you find any bugs or have suggestions for improvements. We would also love to know if anyone finds this tool useful for anything.

The post More fun with contours appeared first on Google Earth Blog.

Catégories: Sites Anglophones

Louisiana flooding as seen in Sentinel imagery

mer 17-08-2016

As we mentioned in Monday’s post, southern Louisiana is experiencing major flooding. One of the worst affected areas is to the east of Baton Rouge, where the Amite River has burst its banks, flooding several neighbourhoods.

We were able to find a Sentinel image captured on August 14th, 2016 which shows the extent of the flooding at that time.

Copernicus Sentinel data, 2016.

And zooming in a bit:

Copernicus Sentinel data, 2016.

To see the image in Google Earth, download this KML file

The original Sentinel image covers a lot more area than we have shown here or included in the KML, but much of the image includes thick cloud cover and we focused on the area with the worst flooding to keep the file size reasonable.

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Catégories: Sites Anglophones

Sentinel imagery coverage

mar 16-08-2016

Yesterday we created a KML file that allows you to preview the latest sentinel imagery using a KML file of all the sentinel tiles. We noticed that some tiles do not have any images available, so we thought it would be interesting to do a map of Sentinel coverage. There are 56,686 tiles, so it took a little while to check all of them to see whether they have imagery and what the date of the latest image is. It turned out that only about half the tiles (27,256) actually have imagery.

We have colour-coded the tiles based on the age (in weeks) of the most recent imagery. As you can see, there is a distinct stripy pattern and the shape of the sun-synchronous orbit is clearly visible. Also of note is that imagery off the coasts and of Antarctica is not as recent as that over the continents.

To see it in Google Earth, download this KML file. Click on any tile to see the date of the latest image available (as of August 15th, 2016).

While creating the above KML we identified a few bugs in yesterday’s KML file, most notably that tiles starting with ‘0’ were incorrectly reporting no imagery. We have fixed the bug, so if you plan to use it then please re-download it from yesterday’s post.

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Catégories: Sites Anglophones

How to quickly preview the latest Sentinel imagery

lun 15-08-2016

[ Update: There were a few bugs in the first version of the KML most notably it reported no imagery for tiles starting with ‘0’. If you intend to use it, please re-download. We have also slightly improved the speed with which it checks for imagery as well as changing the sorting of the displayed images to most recent first. ]

When there are major events such as the ongoing flooding in Louisiana we often check whether the events are visible in Sentinel or Landsat imagery. Given that any given location is only covered about every 10 days by Sentinel and every 16 days by Landsat 8, the first thing we want to check is when the most recent images were taken and whether or not they are any good.

Note: Landsat 7 is offset from Landsat 8 by 8 days, so between them they cover the earth every 8 days, and the Copernicus mission plans to launch a second Sentinel-2 satellite this year, bringing down the revisit frequency to 5 days.

Checking the latest Sentinel imagery is only a few clicks away on Amazon Web Services (AWS), but we thought it would be worth making it even easier.

Simply download this KML file and when you click on a tile, it will check AWS for the latest imagery for that tile and show you previews of the most recent 6 images. If you are interested in one, simply click on the date below the image to go to the appropriate AWS download page. See this post for more on how to download it, process it and view it in Google Earth.

As you can see, quite often, most of the images are not usable as they don’t cover the part of the tile you may be interested in, or there is too much cloud cover.

The main difficulty we encountered while creating the KML was getting our modern JavaScript code to work in Google Earth’s slightly ageing internal browser. The problem is that Google Earth does not show JavaScript errors, so it is a matter of trial and error. Another difficulty was simply working with the KML, because it is so large. The original file was over 100 Mb and although we stripped it down to under 20 Mb by removing unnecessary information and reducing the precision of the coordinates, it still tended to be a bit too much for our text editors.

The post How to quickly preview the latest Sentinel imagery appeared first on Google Earth Blog.

Catégories: Sites Anglophones

Drawing contours with altitude data

ven 12-08-2016

Thank you to GEB reader ‘DJ’ for suggesting this idea in the comments of our post on simulating lakes. It turns out that the algorithm for drawing contours given a regular array of altitude data is actually very simple. We used the algorithm known as ‘Marching Squares’ as described on Wikipedia. For this first attempt we are not going to bother with some of the extras, such as smoothing the final result, or dealing with saddle points.

The main difficulty is getting hold of altitude data. Google offers the Google Maps Elevation API, but it has significant restrictions, including usage limits and most important, rules about what you may do with the data, especially this line:

The Google Maps Elevation API may only be used in conjunction with displaying results on a Google map. It is prohibited to use Google Maps Elevation API data without displaying a Google map.”

If we understand that correctly as well as other restrictions regarding not storing the results, then we are not allowed to use the API to create KML files for use in Google Earth.

We had a look around for other elevation API’s but most of them have similar restrictions, i.e., you can only use them with the providers maps. There are a few that might be less restrictive, such as the ones provided by the USGS and Mapzen, but we will need to investigate them further to double check the permissions as well as how to access the data. There may be others out there. If any of our readers knows of a no-restrictions elevation API, please let us know in the comments.

For now, we have decided to run some tests with the Google Maps API and try to comply with the restrictions.

To use it, start by drawing a polygon in Google Earth, outlining the approximate area you are interested in. Save the polygon as a KML file and select it below. Choose an altitude (in metres) at which you want to draw a contour. Click the ‘Draw contour’ button.
It takes about a minute because of restrictions imposed by the Elevation API on the number of queries you are allowed to make in a given time. For this reason we have used a fairly coarse grid of 100 x 100 points.

input{ padding:5px; color:black; }

KML polygon of the area of interest:

Altitude: m

Draw contour



The final result should be a black contour shown on the Google Map above. As an example, we tried the lake from our post on simulating lakes:

A contour outlining the proposed Batoka Gorge lake

The post Drawing contours with altitude data appeared first on Google Earth Blog.

Catégories: Sites Anglophones

Rio 3D imagery and Google Earth’s elevation data

jeu 11-08-2016

We mentioned in our post on simulating lakes that Google Earth’s elevation data is often very inaccurate in mountainous areas. When looking around the 3D imagery of Rio de Janeiro, where the Olympics are currently taking place, we realised it would be an ideal place to actually check how inaccurate Google Earth’s elevation data can be.

We will start with Sugarloaf Mountain. Drag the slider on the image below to compare Sugarloaf Mountain as seen with the 3D buildings layer turned on or off.
.sliders img{max-width:none; }

Left: With 3D buildings turned on. Right: with 3D buildings turned off

Google Earth actually shows the peak of Sugarloaf Mountain to be at sea level, with an elevation of 0 m. The hill to the right of it fares better with a maximum elevation shown as 33 m. Interestingly, we discovered that when you turn on the 3D buildings layer, Google Earth does actually show elevations from the 3D imagery in the status bar and reports 406 m for the very top of the cable-car building on the top of Sugarloaf Mountain, and 235 m for the top of the tallest tree on the neighbouring hill. So, even assuming Google Earth should show ground level altitudes when the 3D buildings layer is turned off, it is still out by over 400 m for Sugarloaf Mountain.

For the famous statue, “Christ the Redeemer”, the peak is shown at about 500 m with the 3D buildings layer turned off, and about 700 m with it turned on, a difference of over 200 m.

Left: With 3D buildings turned on. Right: with 3D buildings turned off

If we do an elevation profile of the route to the summit at the statue of “Christ the Redeemer”, even with the 3D buildings layer turned on, the elevation data used is the inaccurate data as shown when the 3D buildings layer is off.

The elevation profile shows a dip of over 100 m at the end when it should instead have climbed to an altitude of 700 m.

The above locations are extremes and most other peaks around Rio have smaller discrepancies, but where the terrain is particularly steep the discrepancies can still be significant.

jQuery(document).ready(function() {jQuery(function(){jQuery('.sliders').each(function(i){jQuery(this).beforeAfter({imagePath: '/js/utils/',showFullLinks : false});});});});

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Catégories: Sites Anglophones

Komodo Dragons in Street View

mer 10-08-2016

Google has just released Street View imagery of Komodo Island, Indonesia, as well as significantly increasing the coverage for the rest of Indonesia. There are also a number of new underwater Street View locations in the region, which you can read more about on Google’s Lat Long blog.

Red: New Street View. Blue: Previously existing Street View.

The blue outlines are not showing correctly in Google Earth, but the Street View is there if you know where to look. The blue does show when you zoom out almost to the point where the ‘yellow man’ disappears. To zoom in and out while holding the ‘yellow man’ above the map, use the ‘+’ and ‘-‘ keys on the keyboard. Because of this issue, the imagery is best explored with Google Maps.

Some Komodo dragons relaxing at the beach. View in Google Maps.

The Street View camera was accompanied by guides with sticks to keep the dragons at bay. View in Google Maps.

This Javan deer is apparently not native to the island. View in Google Maps.

The post Komodo Dragons in Street View appeared first on Google Earth Blog.

Catégories: Sites Anglophones

Simulating lakes with Google Earth

mar 09-08-2016

In yesterday’s post we had a KML which includes a polygon showing the approximate extent of a historical lake in Google Earth. We thought it would be worth discussing how to do this and some of the things to look out for.

The basic concept is as simple as drawing a polygon, making it a blue filled polygon (Style, Color tab) and setting its altitude to the desired water level (on the Altitude tab, set to ‘Absolute’ and give an altitude above sea level). For even better results try setting the opacity of the fill to 80%.

One issue that is not easily resolved is the fact that Google Earth does not have very high resolution terrain data and it is often particularly inaccurate in areas with steep slopes, such as mountains or gorges, which are typically the very areas where one would want to simulate a lake. For flatter areas, although the altitude data is often more accurate, gentler slopes mean that for a given altitude error the waterline error will be greater.

Another issue is that Google Earth loads a reduced level of altitude detail as you zoom out, so viewing the results from a distance can be a problem.

For large lakes, the curvature of the earth starts to become a significant issue. This has more of an effect than you might expect. When you draw a polygon in Google Earth and set its altitude to an absolute figure, Google Earth draws each point on the polygon at that altitude, but the lines in between points are straight lines ignoring the curvature of the earth. This means that the centre of the line will be lower than the desired altitude – sometimes significantly so. To get an idea of how much an effect this has, we made the calculator below. For a line 25 km long, the centre of the line will be 12.3 m lower than it should be. A water level difference of 12 m is quite significant for a dam. For longer distances the altitude error grows significantly. At 50 km the error is nearly 50 m and at 100 km it is nearly 200m. If we tried to simulate Lake Kariba, which is around 250 km long, using a single rectangle, the altitude error could be as much as 1,200 m.

input{ padding:5px; color:black; width:50px; }

Length of segment: km
Altitude error at centre: 12.3 m

function getAltitudeError(){ var earthRadius=6371; var distance=document.getElementById('distance').value; var theta=distance/(2*earthRadius); var altitudeError=earthRadius*(1-Math.cos(theta)); altitudeError*=1000; document.getElementById('altitudeError').innerHTML=Math.round(altitudeError * 10) / 10; }

The effect is not limited to lines between points along the edges, but also applies to the shaded water surface. So, for a nearly circular polygon, the maximum error will be in the centre.

To deal with the above problem, the solution is to use polygons with lots of points fairly close together. If you have a long narrow lake, then just adding points all along the sides should be sufficient. If you have a large wide lake you may need to use multiple polygons.

To demonstrate the difference, we will look at a proposed dam on the Batoka Gorge which is along the border between Zambia and Zimbabwe. The proposal is to dam the Zambezi river about 25 km downstream from the Victoria Falls. There are multiple proposals for the final water level of the dam, but one proposal has the water rising almost to the foot of the hydroelectric power station at the Victoria Falls.

We have drawn two rectangles set at exactly the same altitude, one using a simple rectangle, and one using many points along the edges of the gorge. Below you can see the difference in simulated water level between the two polygons.

Purple: a simple rectangle results in a lower water level. Light blue: More points gives higher water levels, which is more accurate.

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

What the whole lake might look like.

At the proposed dam site there is fresh imagery dated June 15th, 2016. This is another example of imagery that Google has added but not yet pushed into ‘historical imagery’. What looks like a new landing strip has been constructed on the Zambian side of the gorge.

The dam construction faces significant opposition from the local tourism industry and others, as the dam would disrupt the white water rafting as well as affecting the local wild wildlife, including some rare bird species that inhabit the george. Read more about it here.

The post Simulating lakes with Google Earth appeared first on Google Earth Blog.

Catégories: Sites Anglophones

Landslide Dams in Google Earth

lun 08-08-2016

Landslide dams are formed when a landslide blocks the course of a river, creating a dam. This can lead to catastrophic consequences, as the dam tends to consist of loose easily eroded material and when the lake fills up and overflows the dam, it can lead to catastrophic collapse and flooding.

We recently came across this story about a possible landslide dam on the Yellow River in China nearly four thousand years ago that may have given way and caused catastrophic floods and be the origin of an ancient Chinese flood story. The original scientific study can be found here. We were able to identify the location in Google Earth and it does look like there was once a dam across the gorge between the two arrows shown below:

Jishi Gorge, Yellow River, China

It took us a while to figure out where the photo in the article was taken from until we realised that there is now a dam further downstream and the river is now a lake, which covers the road that is seen in the photo. Also, Google Earth’s terrain is not very accurate at this location, so the mountains don’t look as steep in Google Earth as they are in reality.

Looking on Wikipedia we found that there are a number of other examples of Landslide dams around the world.

There was a landslide in Tajikistan in 1911 that formed Lake Sarez. At 5 kilometres long, 3.2 kilometres wide and up to 567 metres high, it is the tallest natural dam in the world. There is some concern about its stability and the risk of catastrophic flooding in the event of an earthquake.

The landslide dam at Lake Sarez. Water appears to seep through it, forming a river starting at its base.

In 1925 there was a landslide in Wyoming, USA, known as the Gros Ventre landslide that formed Lower Slide Lake. The dam failed in 1927, causing deadly flooding downstream, but much of the dam and the lake remain to this day.

Gros Ventre landslide

In Montana, USA there is Quake Lake, formed when an earthquake triggered a landslide in 1959.

Quake Lake, Montana. The landslide scar is still clearly visible.

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A landslide in Pakistan in 2010 formed Attabad Lake

This image was captured just a few months after the landslide. Sadly the older imagery doesn’t quite cover the landslide location.

In 2014 in Nepal there was a landslide that blocked the Sunkoshi river. Due to fears of catastrophic failure a canal was dug through the dam.

Before and after of the Sunkoshi landslide.

Another landslide dam was formed in Langtang valley, Nepal in 2015. There is no relevant imagery in Google Earth yet, but you can see it in Landsat imagery here.

For the above locations and a number of other examples of landslide dams in Google Earth, download this KML file

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Catégories: Sites Anglophones

Verne: The Himalayas

ven 05-08-2016

Google has just released a game for Android devices called “Verne: The Himalayas” that makes use of Google Maps’ 3D imagery. The post about it on Google’s Lat Long blog seems to imply that they have used Google’s self-gathered and generated 3D imagery meshes, but this is not the case. The game, as the name suggests, is set in the Himalayas where Google has not released any of its new 3D imagery. The Himalayas are in 3D, but the altitude data, as far as we know, comes from 3rd parties and is not particularly high resolution. The imagery of the region is also entirely from third party sources.

Of course, this is not the first time someone has thought of creating a game with Google Earth’s 3D imagery. Google Earth itself has a built-in flight simulator. There also used to be an excellent flight simulator based on the Google Earth API called “GEFS Online”, but it has since been converted to use CesiumJS due to the deprecation of the Google Earth API. When the Google Earth API was first released, Google included a game called ‘Monster Milktruck’, which can still be played using this post in the Firefox browser. The Google Earth API doesn’t allow game code to detect the new type of 3D imagery, so games like Monster Milktruck have the truck driving through the 3D imagery rather than bumping in to it.

We often get emails from readers wanting access to Google’s 3D data, either the altitude data or the new 3D meshes. They usually want to use it in GIS applications, but some would like to create games with it. However, as far as we know, Google does not allow the data to be extracted from Google Earth or Google Maps and doesn’t have any option to licence the data for other uses.

If Google was to either update the Google Earth API or allow 3D in the Google Maps API we would soon see many games based on it.

The altitude data for the Himalayas has improved over time and is quite impressive, but it is nowhere near as good as Google’s 3D meshes. Compare Mt Everest with some 3D mesh imagery of Muizenberg Peak, Cape Town.

Mt Everest in the Himalayas

The slopes of Muizenberg Peak, Cape Town.

The post Verne: The Himalayas appeared first on Google Earth Blog.

Catégories: Sites Anglophones

Can we see Antarctic seals in Google Earth?

jeu 04-08-2016

We recently came across this interesting article on the DigitalGlobe blog. It is about using imagery crowd-sourcing site Tomnod to count seals in DigitalGlobe satellite imagery of Antarctica. The very best commercial satellite imagery available today has a resolution of about 30 cm per pixel, with most imagery that we call ‘high resolution’ satellite imagery closer to 50 cm per pixel. This usually means that animals are only a few pixels in an image and difficult or impossible to spot. In the cases where we have seen animals in Google Earth it has typically been in aerial imagery, such as the National Geographic Africa Megaflyover images or areas like the US which have a lot of aerial imagery. And there is, of course, Street View.

Antarctica has the advantage that we are looking for dark spots on a fairly featureless white background, which makes spotting seals possible. Much of Antarctica doesn’t have high resolution satellite imagery but there are some areas that have DigitalGlobe strips of imagery. So, given the tips in the DigitalGlobe blog post, we wondered whether we could find any seals in the imagery.

We aren’t certain if these dark spots are seals, but they do match the description given.

This is rather low resolution imagery but it does look like there might be groups of seals gathered around this crack in the ice.

We are fairly sure this is a positive find and that those are seals.

For the locations above, download this KML file.

Despite looking rather white and boring from a distance, it is well worth zooming in on Antarctica and looking around, as there is a remarkable variety of landscapes formed by glaciers and shifting sea ice. For best results switch to ‘historical imagery’ as it is much easier to find the high resolution patches that way.

We also tried looking for seals with the Tomnod website, but were unsuccessful. Google Earth is a much better interface for this sort of thing as it is much easier to find major features, such as cracks in the ice, then zoom in to them. The actual survey the scientists want, however, has to be done on Tomnod, because Google Earth does not have access to all the imagery that they want searched.

The post Can we see Antarctic seals in Google Earth? appeared first on Google Earth Blog.

Catégories: Sites Anglophones

Olympic venues get a 3D update

mer 03-08-2016

Google has recently updated the 3D imagery for some of the key Olympic venues. Sometime in June Google had significantly increased the 3D coverage for Rio de Janeiro but that imagery was captured around February 2013 in preparation for the FIFA World Cup that took place in Brazil in 2014. There is also fresh aerial imagery that we believe was captured at the same time as the 3D imagery. This would put the date of the 3D imagery of the white water and BMX venues as late as July 2nd, 2016. So Google really rushed it, getting the imagery processed in under a month. The aerial imagery of some of the other venues is a bit older (early June). The aerial imagery is available only in the default layer. It has not, as of this writing, been pushed to ‘historical imagery’.

The white water venue looks ready for action.

Google clearly put in some extra modelling effort on some of the buildings:

Maria Lenk Aquatics Centre.

We wanted to post a link to a map of Olympic venues that we found on the Google Maps Gallery a few weeks ago, but when we checked today it appears the Google Maps Gallery has been discontinued and replaced by My Maps. It appears to still be possible to see maps that were formerly in the Maps Gallery, but there is no search function that we could find! So instead, use this KML file for the main Olympic venues that we previously created for this post

“Top Maps” and “Staff Picks” are familiar headings from the Google Maps Gallery. But where is the search box?

The Google Maps Gallery had lost a lot of its best maps last year when it dropped maps from Google Maps Engine, which was deprecated and later shut down.

The post Olympic venues get a 3D update appeared first on Google Earth Blog.

Catégories: Sites Anglophones