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Deep Learning for House Categorisation, a Proposal Towards Automation in Land Registry

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Hybrid Artificial Intelligent Systems (HAIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12344))

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Abstract

Land typology classification is one of the main challenges of Land Registries all around the world. This process has historically been carried out by hand, requiring a large workforce and long processing times. Satellite imagery is shaking up the information retrieval methods for rural areas, where automatic algorithms have also been developed for land categorisation, but never for urban areas. This study provides an algorithm which can potentially speed up the decision-making process, reduce and detect biases; by automatically classifying images of houses facades into land registry categories. Convolutional Neural Networks are combined with a SVM and trained with over 5,000 labelled images. Success rate is above 85% and single image processing time is of the order of milliseconds. Results make it possible to reduce operating costs and to improve the classification performance by taking the human factor out of the equation.

Supported by the project “Intelligent and sustainable mobility supported by multi-agent systems and edge computing (InEDGEMobility): Towards Sustainable Intelligent Mobility: Blockchain-based framework for IoT Security”, Reference: RTI2018–095390-B-C32, financed by the Spanish Ministry of Science, Innovation and Universities (MCIU), the State Research Agency (AEI) and the European Regional Development Fund (FEDER).

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Authors and Affiliations

  1. BISITE Research Group, University of Salamanca, Edificio Multiusos I+D+i, Calle Espejo s/n, 37007, Salamanca, Spain

    David Garcia-Retuerta, Roberto Casado-Vara & Javier Prieto

  2. Department of Industrial Engineering, University of A Coruña, Ferrol, A Coruña, Spain

    Jose L. Calvo-Rolle & Héctor Quintián

Authors
  1. David Garcia-Retuerta
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  2. Roberto Casado-Vara
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  3. Jose L. Calvo-Rolle
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  4. Héctor Quintián
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  5. Javier Prieto
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Corresponding author

Correspondence to David Garcia-Retuerta .

Editor information

Editors and Affiliations

  1. University of Oviedo, Oviedo, Spain

    Enrique Antonio de la Cal

  2. University of Oviedo, Oviedo, Spain

    José Ramón Villar Flecha

  3. University of A Coruña, Ferrol, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Garcia-Retuerta, D., Casado-Vara, R., Calvo-Rolle, J.L., Quintián, H., Prieto, J. (2020). Deep Learning for House Categorisation, a Proposal Towards Automation in Land Registry. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science(), vol 12344. Springer, Cham. https://doi.org/10.1007/978-3-030-61705-9_58

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  • DOI: https://doi.org/10.1007/978-3-030-61705-9_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61704-2

  • Online ISBN: 978-3-030-61705-9

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