Historical imagery density in Google Earth: Part 2

Last week we started on a project to try and map out the density of historical imagery in Google Earth using the Google Earth plugin.

We created a short piece of JavaScript that queries the Google Earth plugin for the number of images available at a given latitude and longitude. We did this every half degree latitude and longitude between 60° N and 60° S and cut it down to every degree longitude between 60° and 80° towards the poles, pausing one second between each one to give Google Earth time to retrieve the data. The result is just over 200,000 points of data, which took several days to generate, running 24 hours a day.

This really gave us a new appreciation of just how big the earth is. DigitalGlobe images cover about a ninth of the size of each square we generated, so at a rough estimate capturing one image per second, would take three weeks to photograph the whole globe. Landsat 8 actually manages to image the whole globe in just 16 days, but takes much lower resolution images, which cover a larger area for each image.

We had discovered with our ‘Chinese map offsets’ map that Google Earth cannot handle very large numbers of icons. However, it seems to have no great difficult with Placemarks that have no icons. You can load the whole dataset without significantly affecting Google Earth’s performance. Download the data here as a KMZ file. You have to zoom in a bit before you see the numbers – which probably helps the performance. If it looks too clustered try changing Google Earth label size to ‘Small’ in Tools->Options->3D View.

The next step was to try and make a heatmap effect. Google Fusion Tables that we used last week has a limit of 1000 points. So we decided to try and create our own in KML. You can download the result here. Again, we are quite impressed by Google Earth’s performance. We did have to optimize it a bit, as our initial attempts did make Google Earth very sluggish. If you have a slow computer you may find even the optimized version is a bit too much when zoomed out. However, performance should improve when you zoom in.

You can make out US state boundaries where aerial imagery sets overlap, and see the Amazon river.

As we noted last week, there are some odd effects in the data, such as high counts just off the coast in many places and some interesting bands in the data. There are also remarkably large numbers in some places. These could be related to how we collected the data or how the Google Earth plugin reports the data.