Machine Learning in Mapmaking

Many of the problems in making digital maps lie in the conversion or translation of data from one form to another - sometimes from a big data source. For example, translation of satellite imagery into a more schematic form like a street view. In this example, a higher dimension or richer data source needs to be condensed into a simpler one. What if the problem is the reverse: Is it possible to move from a lower dimension or quality source to a richer one?

Image processing provides many examples of adding resolution to images. Machine learning provides several techniques to translate lower resolution images to higher ones. Recent work using Generative Adversarial Networks are an example of using ML to add detail to lower resolution imagery.

Many areas of machine learning are driven by the presence of huge amounts of data and the need to quantify and analyze that data for useful (to humans) information. With the explosion of satellite data, machine learning naturally comes into play as a useful analysis tool.

We are exploring some of the opportunities and questions that go along with the abundance of imagery being produced, including:

  1. Visual detection of objects from satellite data
  2. Automated translation and conversion of GIS and satellite data
  3. Augmentation of training data for machine learning algorithms
  4. Dimensionality reduction of large dimension data sets (e.g.. k-means, PCA)