Skyline variations allow estimating distance to trees on landscape photos using semantic segmentation

TL;DR

This paper demonstrates that variations in skyline profiles from landscape photos, analyzed through semantic segmentation and statistical metrics, can be used to estimate distances to trees on the horizon, aiding landscape analysis.

Contribution

The study introduces a novel methodology using skyline variation metrics and semantic segmentation to estimate distances to trees in landscape photos, showing promising results.

Findings

Achieved a maximum R2 score of 0.47 in distance estimation.

Identified a functional relationship between skyline metrics and tree distance.

Demonstrated potential for skyline analysis in landscape property assessment.

Abstract

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.