  Approximate distance estimation can be used to determine fundamental
landscape properties including complexity and openness. We show that variations
in the skyline of landscape photos can be used to estimate distances to trees
on the horizon. A methodology based on the variations of the skyline has been
developed and used to investigate potential relationships with the distance to
skyline objects. The skyline signal, defined by the skyline height expressed in
pixels, was extracted for several Land Use/Cover Area frame Survey (LUCAS)
landscape photos. Photos were semantically segmented with DeepLabV3+ trained
with the Common Objects in Context (COCO) dataset. This provided pixel-level
classification of the objects forming the skyline. A Conditional Random Fields
(CRF) algorithm was also applied to increase the details of the skyline signal.
Three metrics, able to capture the skyline signal variations, were then
considered for the analysis. These metrics shows a functional relationship with
distance for the class of trees, whose contours have a fractal nature. In
particular, regression analysis was performed against 475 ortho-photo based
distance measurements, and, in the best case, a R2 score equal to 0.47 was
achieved. This is an encouraging result which shows the potential of skyline
variation metrics for inferring distance related information.
