Towards Cadastral Intelligence? Extracting visible boundaries from UAV data through image analysis and machine learning

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Abstract

The inability to access formal land registration systems fosters insecure land tenure and conflicts, especially in developing countries. This calls for low-cost and scalable mapping solutions aligning with fit-for-purpose land administration. The work presented in this article supports the UAV-based mapping of land tenure inspired by state-of-the-art approaches from remote sensing, geoinformatics and computer vision. The guiding question is how to develop an automated approach that promotes the paradigm shift towards cadastral intelligence which integrates human-based expert knowledge with automatically generated machine-based knowledge. online version:
Original languageEnglish
Number of pages2
JournalGIM International
Publication statusPublished - 15 May 2019

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land tenure
Unmanned aerial vehicles (UAV)
image analysis
Image analysis
Learning systems
land registration
computer vision
paradigm shift
Developing countries
Computer vision
Remote sensing
developing world
remote sensing
cost
Costs
machine learning
conflict
land
state of the art

Keywords

  • UAV
  • Land administration

Cite this

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abstract = "The inability to access formal land registration systems fosters insecure land tenure and conflicts, especially in developing countries. This calls for low-cost and scalable mapping solutions aligning with fit-for-purpose land administration. The work presented in this article supports the UAV-based mapping of land tenure inspired by state-of-the-art approaches from remote sensing, geoinformatics and computer vision. The guiding question is how to develop an automated approach that promotes the paradigm shift towards cadastral intelligence which integrates human-based expert knowledge with automatically generated machine-based knowledge. online version:",
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AB - The inability to access formal land registration systems fosters insecure land tenure and conflicts, especially in developing countries. This calls for low-cost and scalable mapping solutions aligning with fit-for-purpose land administration. The work presented in this article supports the UAV-based mapping of land tenure inspired by state-of-the-art approaches from remote sensing, geoinformatics and computer vision. The guiding question is how to develop an automated approach that promotes the paradigm shift towards cadastral intelligence which integrates human-based expert knowledge with automatically generated machine-based knowledge. online version:

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