Start with a line, let the planet complete the picture
    
    
    
    
    Jeff Nusz, Data Arts
      Team
      
      Take a break this holiday season and paint with satellite images of the Earth through a new
      experiment called 
Land Lines. The project lets
      you explore Google Earth images in unexpected ways through gesture. Earth provides the
      palette; your fingers, the paintbrush.
      
      There are two ways to explore–drag or draw. "Draw" to find satellite images that
      match your every line. "Drag" to create an infinite line of connected rivers,
      highways and coastlines. Here's a quick demo:
      
      
      
      
      
      Everything runs in real time in your phone's web browser without any servers.
      The responsiveness of the project is a result of using machine learning,
      data optimization, and vantage-point trees to analyze the images and store that data.
      
      We preprocessed the images using a combination of 
Open
      CV's Structured Forests machine learning based edge detection and 
ImageJ's Ridge Detection library. This
      culled the initial
      dataset of over fifty thousand high res images down to just a few thousand
      selected for their presence of lines, as shown in the example below. What
      ordinarily would take days was completed in just a few hours.
      
      
      
      
      
      Example output from the line detection processing. The
      dominant line is
      highlighted in red while secondary lines are highlighted in green.
      
      In the drawing exploration, we stored the resulting data in a 
vantage-point tree. This enabled
      us to efficiently run gesture matching against all the images and have results
      appear in milliseconds.
      
      
      
      
      
      An early example of gesture matching using vantage
      point trees, where the
      drawn input is on the right and the closest results on the left.
      
      
      
      
      Another example of user gesture analysis, where the
      drawn input is on the
      right and the closest results on the left. 
      
      Built in collaboration with Zach Lieberman, 
Land Lines is an experiment
      in big visual data that explores themes of connection. We tried several machine
      learning libraries in our development process. The learnings from that
      experience can be found in the 
case study,
      while the project
      code is available open-source on 
Git
      Hub. Start with a line at 
g.co/landlines.