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Geocarto International Volume 37, 2022 - Issue 12
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Original Articles

An ensemble architecture of deep convolutional Segnet and Unet networks for building semantic segmentation from high-resolution aerial images

Abolfazl Abdollahia Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney (UTS), NSW, AustraliaView further author information
,
Biswajeet Pradhana Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney (UTS), NSW, Australia;b Department of Energy and Mineral Resources Engineering, Sejong University, Gwangjingu, Seoul, Korea;c Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi, Selangor, MalaysiaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0001-9863-2054View further author information
ORCID Icon &
Abdullah M. Alamrid Department of Geology & Geophysics, College of Science, King Saud Univ, Riyadh, Saudi ArabiaView further author information
Pages 3355-3370 | Received 06 Jul 2020, Accepted 08 Nov 2020, Published online: 26 Dec 2020

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Dávid Abriha & Szilárd Szabó. (2023) Strategies in training deep learning models to extract building from multisource images with small training sample sizes. International Journal of Digital Earth 16:1, pages 1707-1724.
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Prakash Ps & Bharath H. Aithal. (2023) Building footprint extraction from very high-resolution satellite images using deep learning. Journal of Spatial Science 68:3, pages 487-503.
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Bo Yu, Fang Chen, Ning Wang, Lu Yang, Haiping Yang & Lei Wang. (2022) MSFTrans: a multi-task frequency-spatial learning transformer for building extraction from high spatial resolution remote sensing images. GIScience & Remote Sensing 59:1, pages 1978-1996.
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Shouhang Du, Wei Li, Jun Li, Shihong Du, Chengye Zhang & Yaqin Sun. (2022) Open-pit mine change detection from high resolution remote sensing images using DA-UNet++ and object-based approach. International Journal of Mining, Reclamation and Environment 36:7, pages 512-535.
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