Building Detection from Aerial Imagery using Inception Resnet UNET and UNET Models

Research output: Contribution to journal › Conference article › Research › peer review

Authors

  • S. Aghayari
  • A. Hadavand
  • S. Mohamadnezhad Niazi
  • Mohammad Omidalizarandi

Research Organisations

External Research Organisations

  • Ideh Pardazan Tosseah Consulting Engineering Company
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Details

Original languageEnglish
Pages (from-to)9-17
Number of pages9
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume10
Issue number4/W1-2022
Publication statusPublished - 13 Jan 2023
Event6th SMPR and 4th GIResearch, ISPRS Geospatial Conference -
Duration: 19 Feb 2023 → 22 Feb 2023

Abstract

Buildings are one of the key components in change detection, urban planning, and monitoring. The automatic extraction of the building from high-resolution aerial imagery is still challenging due to the variations in their shapes, structures, textures, and colours. Recently, the convolutional neural networks (CNN) show a significant improvement in object detection and extraction that surpasses other methods. To extract building, in this paper two segmentation architectures, the UNet and the Inception ResNet UNet are implemented and then tested on the Inria aerial image datasets. The Inception ResNet UNet utilizes the Inception architecture and residual blocks. This makes the model wide and deep, though there are a few differences between numbers of UNet and Inception ResNet UNet parameters. The analyses show that UNet has a high rate of metrics in the training progress. However, on the unseen dataset, Inception ResNet UNet extracts buildings more accurately (97.95% accuracy and 0.96 in the dice metric) in comparison with UNet (94.30% accuracy and 0.55 in the dice metric).

Keywords

    Large-scale monitoring, Building detection, Image segmentation, Residual blocks, Skip connection

ASJC Scopus subject areas

Cite this

Building Detection from Aerial Imagery using Inception Resnet UNET and UNET Models. / Aghayari, S.; Hadavand, A.; Mohamadnezhad Niazi, S. et al.
In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 10, No. 4/W1-2022, 13.01.2023, p. 9-17.

Research output: Contribution to journal › Conference article › Research › peer review

Aghayari, S, Hadavand, A, Mohamadnezhad Niazi, S & Omidalizarandi, M 2023, 'Building Detection from Aerial Imagery using Inception Resnet UNET and UNET Models', ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 10, no. 4/W1-2022, pp. 9-17. https://doi.org/10.5194/isprs-annals-X-4-W1-2022-9-2023
Aghayari, S., Hadavand, A., Mohamadnezhad Niazi, S., & Omidalizarandi, M. (2023). Building Detection from Aerial Imagery using Inception Resnet UNET and UNET Models. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 10(4/W1-2022), 9-17. https://doi.org/10.5194/isprs-annals-X-4-W1-2022-9-2023
Aghayari S, Hadavand A, Mohamadnezhad Niazi S, Omidalizarandi M. Building Detection from Aerial Imagery using Inception Resnet UNET and UNET Models. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2023 Jan 13;10(4/W1-2022):9-17. doi: 10.5194/isprs-annals-X-4-W1-2022-9-2023
Aghayari, S. ; Hadavand, A. ; Mohamadnezhad Niazi, S. et al. / Building Detection from Aerial Imagery using Inception Resnet UNET and UNET Models. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2023 ; Vol. 10, No. 4/W1-2022. pp. 9-17.
Download
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abstract = "Buildings are one of the key components in change detection, urban planning, and monitoring. The automatic extraction of the building from high-resolution aerial imagery is still challenging due to the variations in their shapes, structures, textures, and colours. Recently, the convolutional neural networks (CNN) show a significant improvement in object detection and extraction that surpasses other methods. To extract building, in this paper two segmentation architectures, the UNet and the Inception ResNet UNet are implemented and then tested on the Inria aerial image datasets. The Inception ResNet UNet utilizes the Inception architecture and residual blocks. This makes the model wide and deep, though there are a few differences between numbers of UNet and Inception ResNet UNet parameters. The analyses show that UNet has a high rate of metrics in the training progress. However, on the unseen dataset, Inception ResNet UNet extracts buildings more accurately (97.95% accuracy and 0.96 in the dice metric) in comparison with UNet (94.30% accuracy and 0.55 in the dice metric).",
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AU - Aghayari, S.

AU - Hadavand, A.

AU - Mohamadnezhad Niazi, S.

AU - Omidalizarandi, Mohammad

N1 - Funding Information: This research was supported by Ideh Pardazan Tosseah Consulting Engineering Company. We are grateful to all of those with whom we have had the pleasure to work during this and other related projects.

PY - 2023/1/13

Y1 - 2023/1/13

N2 - Buildings are one of the key components in change detection, urban planning, and monitoring. The automatic extraction of the building from high-resolution aerial imagery is still challenging due to the variations in their shapes, structures, textures, and colours. Recently, the convolutional neural networks (CNN) show a significant improvement in object detection and extraction that surpasses other methods. To extract building, in this paper two segmentation architectures, the UNet and the Inception ResNet UNet are implemented and then tested on the Inria aerial image datasets. The Inception ResNet UNet utilizes the Inception architecture and residual blocks. This makes the model wide and deep, though there are a few differences between numbers of UNet and Inception ResNet UNet parameters. The analyses show that UNet has a high rate of metrics in the training progress. However, on the unseen dataset, Inception ResNet UNet extracts buildings more accurately (97.95% accuracy and 0.96 in the dice metric) in comparison with UNet (94.30% accuracy and 0.55 in the dice metric).

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