Mono-temporal Gis Update Assistance System Based on Unsupervised Coherence Analysis and Evolutionary Optimisation

Data in Geo Information Systems (GIS) is used for map services and various applications. Thus, quality assessment on a regular basis is required to keep the data up-to-date. In this paper we focus on one key reasons for updates: incorrect object borders. State of the art systems semi-automatically analyse up-to-date satellite image data to narrow down areas that have to be considered for GIS updates. Often resources are limited and only data from one point in time is available that is compared to the data. Rule based systems are required to bridge the gap between GIS specifications and results from image analysis. We present a system that can find areas of change without any manual configuration. Our approach automatically learns about important aspects of GIS specifications by analysing correct GIS objects. In potentially out-dated GIS data still a majority of objects is unchanged. Thus, we derive an model for normality (= correctness) by evaluating the coherence of relations between GIS objects and image analysis results. We synthesise changes at GIS object borders and analyse the impact on normality. In an evolutionary optimisation we determine areas of change that are rated with a significance value. We show that we can find 83% of all relevant update areas with a precision of 0.18, not considering the significance of changes. Including significance we can push the precision to 0.26 while still finding 77% of all relevant update areas.