Shadow Pattern-Enhanced Building Height Extraction Using Very-High-Resolution Image

Xiran Zhou, Soe W. Myint

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Building height is valuable for a variety of foci in urban studies. The traditional field investigations are not practical for the updates of massive building height in a large-scale urban area. Given the relationship between building structures and their shadow sizes, the building shadow becomes practical for estimating its corresponding building height when its geometrical shape is visible in newly emerging very-high-resolution (VHR) images. However, the shadow shape of different buildings might vary significantly, posing a great challenge to determining the edge of shadow useful for predicting building height. This study proposes a shadow pattern classification system (ShadowClass) to summarize the varied shadow shapes into a number of pattern categories and employ a cutting-edge CNN model to classify the extracted shadows into a pattern for automatically determining the edge of a building shadow being useful for building height estimation. We integrated the proposed approach into two branches of the state-of-the-art approaches: shadow-based building height estimation with open cyberinfrastructure and shadow-based building height estimation with VHR image. The experimental results proved that the proposed method could be a practical solution for single and isolated buildings that have their complete shadow shape.

Original languageEnglish (US)
Pages (from-to)180-190
Number of pages11
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume16
DOIs
StatePublished - 2023

Keywords

  • Building height estimation
  • convolutional neural networks
  • remote sensing image
  • shadow extraction
  • shadow pattern

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Atmospheric Science

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