  Existing performance measures rank delineation algorithms inconsistently,
which makes it difficult to decide which one is best in any given situation. We
show that these inconsistencies stem from design flaws that make the metrics
insensitive to whole classes of errors. To provide more reliable evaluation, we
design three new metrics that are far more consistent even though they use very
different approaches to comparing ground-truth and reconstructed road networks.
We use both synthetic and real data to demonstrate this and advocate the use of
these corrected metrics as a tool to gauge future progress.
