Extracting cadastral boundaries from uav images using fully convolutional networks

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Abstract

The cadastre is the foundation of land management. However, it is estimated that 70% of the land rights in the world remain unregistered. Traditional approaches are costly and labor intensive, therefore, recently the use of remotely sensed images has been investigated. The delineation of cadastral boundaries from such data is challenging since not all boundaries are demarcated by visible physical objects. In this paper, we introduce a technique based on deep Fully Convolutional Networks (FCNs), which can automatically learn high-level spatial features from images, to extract cadastral boundaries. Our strategy combines FCN and a grouping algorithm using the Oriented Watershed Transform (OWT) to generate connected contours. We carried out an experimental analysis in a real case study in Busogo, Rwanda, using images acquired by Unmanned Aerial Vehicles (UAV) in 2018. Our investigation shows promising results in automatically extracting visible boundaries, which can contribute to the current mapping and updating practices in Rwanda.
Original languageEnglish
Title of host publicationIGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
Place of PublicationYokohama
PublisherIEEE
Pages2455-2458
Number of pages4
ISBN (Electronic)978-1-5386-9154-0
DOIs
Publication statusPublished - Nov 2019
EventIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan
Duration: 28 Jul 20192 Aug 2019

Conference

ConferenceIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Abbreviated titleIGARSS 2019
CountryJapan
CityYokohama
Period28/07/192/08/19

Keywords

  • Deep learning

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  • Cite this

    Xia, X., Koeva, M. N., & Persello, C. (2019). Extracting cadastral boundaries from uav images using fully convolutional networks. In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium (pp. 2455-2458). Yokohama: IEEE. https://doi.org/10.1109/IGARSS.2019.8898156