Map Change Prediction for Quality Assurance

Abstract

Open geospatial datasources like OpenStreetMap are created by a community of mappers of different experience and with different equipment available. It is therefore important to assess the quality of Open-StreetMap-like maps to give recommendations for users in which situations a map is suitable for their needs. In this work we want to use already defined ways to assess the quality of geospatial data and apply them to a Machine Learning algorithm to classify which areas are likely to change in future revisions of the map. In a next step we intend to qualify the changes detected by the algorithm and try to find causes of the changes being tracked.