Transformer-based semantic segmentation for large-scale building footprint extraction from very-high resolution satellite images-Reference-Cited by-同舟云学术

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Transformer-based semantic segmentation for large-scale building footprint extraction from very-high resolution satellite images

Published:2024-05 Issue:10 Volume:73 Page:4937-4954
ISSN:0273-1177
Container-title:Advances in Space Research
language:en
Short-container-title:Advances in Space Research

Author:

Gibril Mohamed Barakat A.,Al-Ruzouq Rami,Shanableh Abdallah,Jena Ratiranjan,Bolcek Jan,Shafri Helmi Zulhaidi Mohd,Ghorbanzadeh Omid^ORCID

Funder

BOKU

Publisher

Elsevier BV

Reference77 articles.

1. An ensemble architecture of deep convolutional Segnet and Unet networks for building semantic segmentation from high-resolution aerial images;Abdollahi;Geocarto. Int.,2022

2. A spatio-temporal framework for sustainable planning of buildings based on carbon emissions at the city scale;Abu Dabous;Sustain Cities Soc.,2022

3. ADE20K dataset. [accessed 2023 July 6]. https://groups.csail.mit.edu/vision/datasets/ADE20K/

4. Sensors, features, and machine learning for oil spill detection and monitoring: a review;Al-Ruzouq;Remote Sens.,2020

5. Automatic building extraction on satellite images using unet and ResNet50;Alsabhan;Comput Intell Neurosci.,2022

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