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U-Net: Deep Learning for Extracting Building Boundary Collected by Drone of Agadir’s Harbor

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Digital Technologies and Applications (ICDTA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 211))

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Abstract

Identification of a specific object in an image might be a trivial task for humans but are often quite challenging for machines. Recently the field has witnessed groundbreaking research with cutting-edge results. However, for real-world application problems of this research remain a challenge. The approach used is based on a training model from a Dataset, and this model will be used in all processing to detect homes from sample images. All the images were extracted from the unmanned aerial vehicle (UAV) recordings. This paper presents a method to detect segmentation of the Building footprint using U-Net architecture; in order to build footprints without needing manual digitizing with higher accuracy.

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Author information

Authors and Affiliations

  1. Faculty of Sciences, Ben M’sick-University Hassan II, Casablanca, Morocco

    Tarik Chafiq, Hayat Hachimi, Mohammed Raji & Soufiane Zerraf

Authors
  1. Tarik Chafiq
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  2. Hayat Hachimi
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  3. Mohammed Raji
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  4. Soufiane Zerraf
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Editor information

Editors and Affiliations

  1. ENSA, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Saad Motahhir

  2. Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Badre Bossoufi

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Chafiq, T., Hachimi, H., Raji, M., Zerraf, S. (2021). U-Net: Deep Learning for Extracting Building Boundary Collected by Drone of Agadir’s Harbor. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2021. Lecture Notes in Networks and Systems, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-030-73882-2_11

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