Increasing Efficiency of Road Extraction by Self-Diagnosis

The aim of self-diagnosis (internal evaluation) is to determine the geometric and semantic accuracy of the extracted objects during the extraction process. This article presents general ideas on the methodology, implementation, and representation of self-diagnosis within automatic object extraction systems. The authors demonstrate how results attached with confidence values can increase system efficiency for practical applications. They show the potential of self-diagnosis by two road extraction approaches, based on a test series of aerial images, that have been extended by a self-diagnosis component. The authors note that, in order to speed up the time- and cost-intensive inspection, the system should provide the operator with confidence values characterizing its own performance. In practice, however, this is rarely the case. The authors conclude that self-diagnosis is a very helpful tool to guide the user through the road network by pointing to such parts where problems or uncertainties occurred during the extraction. The self-diagnosis also shows great potential to improve an automatic extraction, mainly due to less sensitivity against improper parameter settings. However, there are some deficiencies in distinguishing between erroneous and uncertain, but correct, extractions.