From an operational standpoint, road extraction remains largely a manual process despite the existence of several
commercially available automation tools. The problem of <i>automated feature extraction </i>(AFE) in general is a challenging
task as it involves the recognition, delineation, and attribution of image features. The efficacy of AFE algorithms in
operational settings is difficult to measure due to the inherent subjectivity involved. Ultimately, the most meaningful
measures of an automation method are its effect on productivity and actual utility. Several quantitative and qualitative
factors go into these measures including spatial accuracy and timed comparisons of extraction, different user training
levels, and human-computer interface issues.
In this paper we investigate methodologies for evaluating automated road extraction in different operational
modes. Interactive and batch extraction modes of automation are considered. The specific algorithms investigated are the
GeoEye Interactive Road Tracker®(IRT) and the GeoEye Automated Road Tracker®(ART) respectively. Both are
commercially available from GeoEye. Analysis metrics collected are derived from timed comparisons and spatial
delineation accuracy. Spatial delineation accuracy is measured by comparing algorithm output against a manually
derived image reference. The effect of object-level fusion of multiple imaging modalities is also considered.
The goal is to gain insight into measuring an automation algorithm's utility on feature extraction productivity.
Findings show sufficient evidence to demonstrate a potential gain in productivity when using an automation method
when the situation is warranted. Fusion of feature layers from multiple images also demonstrates a potential for
increased productivity compared to single or pair-wise combinations of feature layers.