Publication detail

Transformer-based Semantic Segmentation for Large-Scale Building Footprint Extraction from Very-High Resolution Satellite Images

GIBRIL, M. AL-RUZOUQ, R. SHANABLEH, A. JENA, R. BOLCEK, J. ZULHAIDI MOHD SHAFRI, H. GHORBANZADEH, O.

Original Title

Transformer-based Semantic Segmentation for Large-Scale Building Footprint Extraction from Very-High Resolution Satellite Images

Type

journal article in Web of Science

Language

English

Original Abstract

Extracting building footprints from extensive very-high spatial resolution (VHSR) remote sensing data is crucial for diverse applications, including surveying, urban studies, population estimation, identification of informal settlements, and disaster management. Although convolutional neural networks (CNNs) are commonly utilized for this purpose, their effectiveness is constrained by limitations in capturing long-range relationships and contextual details due to the localized nature of convolution operations. This study introduces the masked-attention mask transformer (Mask2Former), based on the Swin Transformer, for building footprint extraction from large-scale satellite imagery. To enhance the capture of large-scale semantic information and extract multiscale features, a hierarchical vision transformer with shifted windows (Swin Transformer) serves as the backbone network. An extensive analysis compares the efficiency and generalizability of Mask2Former with four CNN models (PSPNet, DeepLabV3+, UpperNet-ConvNext, and SegNeXt) and two transformer-based models (UpperNet-Swin and SegFormer) featuring different complexities. Results reveal superior performance of transformer-based models over CNN-based counterparts, showcasing exceptional generalization across diverse testing areas with varying building structures, heights, and sizes. Specifically, Mask2Former with the Swin transformer backbone achieves a mean intersection over union between 88% and 93%, along with a mean F-score (mF-score) ranging from 91% to 96.35% across various urban landscapes.

Keywords

remote sensing; satellite imagery; Mask2former; CNN; Swin Transformer; vision transformer

Authors

GIBRIL, M.; AL-RUZOUQ, R.; SHANABLEH, A.; JENA, R.; BOLCEK, J.; ZULHAIDI MOHD SHAFRI, H.; GHORBANZADEH, O.

Released

9. 3. 2024

Publisher

Elsevier

ISBN

1879-1948

Periodical

ADVANCES IN SPACE RESEARCH

Year of study

73

Number

10

State

United Kingdom of Great Britain and Northern Ireland

Pages from

4937

Pages to

4954

Pages count

17

URL

https://www.sciencedirect.com/science/article/pii/S0273117724002205

Full text in the Digital Library

http://hdl.handle.net/11012/245513

BibTex

@article{BUT188212,
  author="Mohamed Barakat A. {Gibril} and Rami {Al-Ruzouq} and Abdallah {Shanableh} and Ratiranjan {Jena} and Jan {Bolcek} and Helmi {Zulhaidi Mohd Shafri} and Omid {Ghorbanzadeh}",
  title="Transformer-based Semantic Segmentation for Large-Scale Building Footprint Extraction from Very-High Resolution Satellite Images",
  journal="ADVANCES IN SPACE RESEARCH",
  year="2024",
  volume="73",
  number="10",
  pages="4937 --4954",
  doi="10.1016/j.asr.2024.03.002",
  issn="1879-1948",
  url="https://www.sciencedirect.com/science/article/pii/S0273117724002205"
}