mlsanityData to 7 Oct 2026

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

arXiv 2201.08816 ↗First posted 14 Jan 2022cs.CV eess.IV stat.APcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 51 references that are in the map. First posted on arXiv 14 Jan 2022.

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References · 11

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  3. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  4. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
  5. 2018Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videoscited by 155
  6. 2014Hypercolumns for Object Segmentation and Fine-grained Localizationcited by 128
  7. 2019Deep Semantic Segmentation of Natural and Medical Images: A Reviewcited by 124
  8. 2019A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D imagescited by 29
  9. 2015High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and its Applications to High-Level Visioncited by 24
  10. 2018Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detectioncited by 4
  11. 2021Resource Efficient Mountainous Skyline Extraction using Shallow Learningcited by 3

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