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Authors: Georgios-Fotios Angelis ; Armando Domi ; Alexandros Zamichos ; Maria Tsourma ; Ioannis Manakos ; Anastasios Drosou and Dimitrios Tzovaras

Affiliation: Information and Technologies Institute, Centre for Research and Technology Hellas, 6km. Harilaou - Thermi, Thessaloniki, Greece

Keyword(s): Remote Sensing, Transformers, Building, Extraction, Segmentation.

Abstract: Data visualization has received great attention in the last few years and gives valuable assets for better understanding and extracting information from data. More specifically, in Geospatial data, visualization includes information about the location, the geometric shape of elements, and the exact position of elements that can lead in enhances downstream applications such as damage detection, building energy consumption estimation, urban planning and change detection. Extracting building footprints from remote sensing (RS) imagery can help in visualizing damaged buildings and separate them form terrestrial objects. Considering this, the current manuscript provides a detailed comparison and a new benchmark for remote sensing building extraction. Experiments are conducted in three publicly available datasets aiming to evaluate accuracy and performance of the compared Transformer-based architectures. MiTNet and other five transformers architectures are introduced, namely DeepViTU Net, DeepViTUNet++, Coordformer, PoolFormer, EfficientFormer. In these choices we study design adjustments in order to obtain the best trade off between computational cost and performance. Experimental findings demonstrate that MitNet, which learns features in a hierarchical manner can be established as a new benchmark. (More)

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Paper citation in several formats:
Angelis, G.; Domi, A.; Zamichos, A.; Tsourma, M.; Manakos, I.; Drosou, A. and Tzovaras, D. (2023). A Comparative Study on Vision Transformers in Remote Sensing Building Extraction. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - IVAPP; ISBN 978-989-758-634-7; ISSN 2184-4321, SciTePress, pages 222-229. DOI: 10.5220/0011787800003417

@conference{ivapp23,
author={Georgios{-}Fotios Angelis. and Armando Domi. and Alexandros Zamichos. and Maria Tsourma. and Ioannis Manakos. and Anastasios Drosou. and Dimitrios Tzovaras.},
title={A Comparative Study on Vision Transformers in Remote Sensing Building Extraction},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - IVAPP},
year={2023},
pages={222-229},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011787800003417},
isbn={978-989-758-634-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - IVAPP
TI - A Comparative Study on Vision Transformers in Remote Sensing Building Extraction
SN - 978-989-758-634-7
IS - 2184-4321
AU - Angelis, G.
AU - Domi, A.
AU - Zamichos, A.
AU - Tsourma, M.
AU - Manakos, I.
AU - Drosou, A.
AU - Tzovaras, D.
PY - 2023
SP - 222
EP - 229
DO - 10.5220/0011787800003417
PB - SciTePress