Today we are starting to take a more detailed look at specific regions of the world.

Africa

As we have explained in the past, Google Earth has several ‘background imagery’ data sets that it uses when there is no good quality high resolution satellite imagery available. In Africa there are two distinct sets of ‘background imagery’.

     Across the equatorial region of Africa and a curious strip in the east of Libya, the background imagery is low resolution Landsat data, essentially the same as is seen globally when zoomed out. This background imagery shows through most noticeably where there are rain forests. We believe the lack of higher resolution satellite imagery there may be due to the high frequency of cloud cover.

Lake Victoria is an exception and has imagery attributed to TerraMetrics which, according to this page, used to supply the global ‘background image’ for Google Earth.

     For the rest of Africa, the default background imagery is slightly higher resolution satellite imagery from CNES/Spot Image. It is most visible across the Sahara. We believe the absence of higher resolution imagery over the Sahara is a combination of lack of interest due to the low population and difficult photographic conditions. It is notable that much of the high resolution satellite imagery in the region is false colour imagery suggesting that visible light imagery is difficult to capture because of the bright desert sands.

     Most of Africa has reasonably good high resolution satellite imagery coverage supplied by DigitalGlobe and CNES/Astrum. It is fairly easy to visually distinguish between the two in historical imagery, as the CNES/Astrum imagery has a greenish tint to it. Also, the CNES/Astrum imagery tends to be in approximately equal-sided parallelograms, whereas about half the DigitalGlobe imagery is in strips covering one degree of longitude or latitude.

     Only two spots of Aerial imagery were picked up by our map: Bloemfontein and Cape Town, both in South Africa. There is more aerial imagery in South Africa, but our dataset is not high enough resolution to pick it up.

Europe

The obvious band at 60° north is explained in this post.

It is important to note here that the data we are looking at is based on maps created using the Google Maps API. See this page as an example. One significant difference between Google Maps created this way is that they do not show 3D imagery. Europe has a significant amount of 3D imagery, so what we see in the Google Maps API generated map may be quite different from what can be seen on the standard Google Maps website or in Google Earth.

     As with Africa, Europe has two sets of ‘background imagery’. Low resolution Landsat data can be seen in Ireland, Austria and the northern parts of Norway, Sweden and Finland.

     The second slightly higher resolution set of ‘background imagery’ comes from CNES/Spot Image.

                 Most of Western Europe is a patchwork of aerial imagery of various resolutions from a wide variety of suppliers, including a significant amount collected by Google itself. There is a distinct transition to satellite imagery over Eastern Europe, more obvious in ‘historical imagery’ than in the ‘maximum zoom’ data. Poland and Romania do have some aerial imagery and the country of Montenegro stands out as having its own patch of aerial imagery almost exactly matching its borders. Northern Scandinavia and Ireland are somewhat lacking in aerial imagery.

The Middle East and Central Asia

     Landsat imagery is used as a ‘background image’ for Saudi Arabia, most of Russia, and parts of Kyrgyzstan

     The rest of this region uses Cnes/Spot Image imagery as the ‘background image’. Iraq and Afghanistan really stand out because they have not had any imagery updates for several years. We believe this is deliberate censorship due to the wars in those countries. The sparsity of high resolution satellite imagery in Saudi Arabia and northern Asia is probably, as with the Sahara, a combination of lack of interest due to the low population and difficult photographic conditions. Saudi Arabia has bright sand, and northern Asia has snow cover and poor light for much of the year.

     Pakistan and India have remarkably good coverage with high resolution satellite imagery. Nepal, Bhutan and Bangladesh are also pretty well covered.

There appears to be no aerial imagery in this region. We believe some countries have not allowed Google to gather aerial imagery and for others there may simply be a lack of local suppliers.

We will continue with further regions of the globe in a later post.

The post Google Maps API Maximum Zoom – Part 3: Starting on a more detailed look appeared first on Google Earth Blog.

Google has continued to release 3D imagery and we apologize for being behind with our KML of 3D areas. It is notable that a significant proportion of 3D updates are updated areas rather than entirely new areas. It is time Google started providing dates with the imagery and possibly a ‘historical 3D imagery’ option.

Street View was added to Kenyan parks and the Philippines, and there were also additions in Argentina and Indonesia.

We had a look at the California wild fires using Landsat imagery. Thank you to GEB reader Christiaan Adams for letting us know in the comments about some false colour Digital Globe imagery available via this KML file from Google Crisis Response. Also thank you to GEB reader Robert Tissell for pointing us to other fire detection resources based on VIIRS data, including this website.

We had a look at a way to find recent imagery using the Google Earth plugin. We mapped out the imagery newer than July 1st, 2015 and classified it by imagery supplier. See the results with this KML file. We also had a look at some interesting sights we found in the imagery while classifying it. Thank you to GEB reader Raul for providing some interesting information in the comments about the demolished houses we found.

We noticed that the country of Moldova has different overview imagery from the rest of the globe. Thank you to GEB reader ‘S. H.’ for letting us know that the change was made circa August 19th, 2015. We still do not know why the change was made.
 
 

We experimented with using Google Earth Pro’s bulk geocoding feature for geocoding regions such as suburbs, cities and countries. Although it is clearly a useful feature and is fairly easy to use, there is room for improvement. A feature we would really like to see is a geocoder that can return the outline of an area rather than a simple placemark. If any of our readers knows of any such geocoding service, please let us know.

We had a look at an image released by the European Space Agency showing the surface deformation from the Chile Earthquake.
 
 
 
 

We have just started a series of posts (Part 1: Data Collection, Part 2: Overview ), having a look at the data we can get from the Google Maps API Maximum Zoom Imagery Service. We will continue to look at the data in October.

The post The best of Google Earth for September 2015 appeared first on Google Earth Blog.

Today we are looking at general features of the data.

Key
First of all, we need to know what the colours mean. The colours represent the various available zoom levels in Google Maps, starting with low resolution at 7 to high resolution at 22. This can be roughly interpreted as follows:

Google Maps via API
To better understand Google Maps Zoom levels or to explore the data for a given location, you can use this page, which shows a full screen Google Map using the Google Maps API. It behaves a little differently from the standard Google Maps website. The standard Google Maps website restricts how far you can zoom in based on the imagery available, however, it always shows imagery. The above map, obtained via the API, lets you zoom in beyond the maximum prefered amount, and when you do it displays map tiles labelled “Sorry, we have no imagery here”.

Poles
You will notice in our map that there are noticeable bands in the data towards the poles at 60° and 75° latitude, both north and south. We believe these are an artifact of the Google Maps projection, which spreads out the poles, thus magnifying any imagery towards the poles and in consequence requiring less zoom in order to see a given resolution of imagery.

In our data collection we only went 80° north and south. Google Maps itself only goes to 85° north and south as a consequence of its map projection.

Google Earth has noticeable bands in the actual imagery at 80° north across Greenland and about 82.6° south in Antarctica. These are actual changes in the imagery datasets. The absence of high resolution near the poles may be due to the orbits of the imaging satellites or ‘sun angle constraints’.

Oceans
The spider web of tracks across the oceans noticeable in our data reflect the paths of ships equipped with sonar for mapping the ocean floor. The tracks of higher resolution imagery are clearly visible in Google Earth and have in the past been mistaken for Atlantis or an alien base.

In our next post in this series we will be looking in a bit more detail at the various types of imagery found over land.

The post Google Maps API Maximum Zoom – Part 2: Overview appeared first on Google Earth Blog.

We recently discovered that the Google Maps API provides a service known as the Maximum Zoom Imagery Service, which allows you to find out what the maximum zoom available in Google Maps is for a given latitude and longitude. The maximum zoom available when in ‘Earth’ mode is dependent on what imagery is available in Google Maps. Since the imagery in Google Maps is almost the same as the default layer in Google Earth, this service can tell us a lot about the imagery in Google Earth.

Today we are just looking at how we gathered the data and prepared it for viewing in Google Earth. In later posts we will look at what is actually in the data and what we can learn about Google Maps and Google Earth imagery.

We queried the Maximum Zoom Imagery Service for every 0.1° from -80° to +80° latitude and every 0.1° of longitude. The result is 5.76 million points of data, which results in multiple KMLs totalling over 1Gb. To display it as a heat map we could have created a KML file with each rectangle as a polygon, but that would probably have crashed Google Earth. So instead we used the technique we used when finding imagery updates and converted the data to an image and displayed that using an image overlay.

If you display each data point as a single pixel, then Google Earth tends to blur the image, so we resized the image to have each data point 4 X 4 pixels. The Maximum Zoom available in Google Maps for the locations we collected varies from 7 to 22, 7 being the lowest resolution imagery and 22 being the highest resolution imagery.

To see it for yourself in Google Earth download this KMl file

The post Google Maps API Maximum Zoom – Part 1: Data collection appeared first on Google Earth Blog.

Now the European Space Agency has released a similar map showing the surface deformation from the Chile Earthquake.

To see it in Google Earth download this KML file

The ‘Earthquakes’ layer in Google Earth does not show the Chilean earthquake yet. However, the USGS provides up to date KML files on this web page

We chose this one, which shows all earthquakes over magnitude 2.5 from the last 30 days.

As you can see below, the main quake (largest circle) was further south than you might have expected it to be based on the deformation pattern. The cluster of aftershocks, however, do seem to centre on the same location as the deformation pattern.

The post Surface deformation after Chile earthquake appeared first on Google Earth Blog.

• We have looked at a number of resources for tracking wild fires in the past. The best ones include:

• The US Department of Agriculture Forest Service provides a variety of fire information via KMLs. To get all the data at once download this KML.

• One of the sources of data used is NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) whose data can be obtained directly from NASA via this web page. NASA provides MODIS data for the whole globe. You can get fire data for a particular region of the earth, or download KMLs with data for the whole globe. Keep in mind that the the global ones are quite big and may cause performance issues in Google Earth.

The Californian government provides a website called CAL FIRE which includes a lot of information about fires, including this map of wild fires in California showing the actual extents of the fires. You can view it in Google Earth with this KML file.

As far as we can tell, the most recent imagery in Google Earth is from August 18th, so we cannot currently see the damage caused by the fires. However, it is possible to download Landsat imagery and view it in Google Earth and actually see some of the land affected by the recent fires. We previously experimented with getting Landsat data into Google Earth. The method we showed you required some rather large downloads. We have since found that if quality is not an issue then the easiest way to quickly check Landsat imagery is as follows:

• The following instructions require Google Earth Pro (which is free).
• Start with USGS Earth Explorer. Find the location you are interested in and mark it on the map.
• On the datasets tab, select Landsat Archive->L8 OLI/TIRS
• Click the ‘Results’ button.
• Choose an image closest to the date you are interested in. Ignore any night time images (almost entirely black). In our case we found one covering part of California just north of San Francisco, captured on September 20th.
• Click the icon next to the image labelled ‘download options’.
• Download the file labelled “LandsatLook images with Geographic Reference”.
• Unzip the downloaded files to a folder on your computer.
• Find the file named with just the Landsat reference number and a .jpg extension. Drag it to Google Earth Pro.
• Google Earth Pro automatically detects where to place it and creates an image overlay for you. However, it says that the image exceeds the maximum allowed size. You can choose to either crop the image, or scale it. In our case we chose to scale it.

This is what the result looks like in Google Earth:

To view it in Google Earth you can simply download this KML file.

Comparing this with the above mentioned CAL FIRE map, we find that the two large brown patches south-east of Clear Lake almost exactly match the outlines given for Valley Fire, Jerusalem Fire and Rocky Fire.

Getting the outlines to show on top of the image overlay required adjusting their altitude to 1000m above ground.

Fires detected by MODIS in the last 24 hours in central Africa. There are actually far more fires in Africa right now than in California. Hopefully they are less devastating.

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