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First Steps Predicting Execution of Civil Works from Georeferenced Infrastructure Data

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17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022) (SOCO 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 531))

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Abstract

Geospatial data treatment is an important task since it is a big part of big data. Nowadays, geospatial data exploitation is lacking in terms of artificial intelligence. In this work, we focus on the usage of an machine learning models to exploit a geospatial data. We will follow a complete workflow from the collection and first descriptive analysis of the data to the preprocess and evaluation of the different machine learning algorithms. From unload dataset we will predict if the unload will lead to civil work, in other words, it is a classification problem. We conclude that combining machine learning and geospatial data we can get a lot out of it.

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Acknowledgements

This work has been partially funded by Ministerio de Ciencia e Innovacion from Spain under the project PID2020-116346GB-I00

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Authors and Affiliations

  1. TECNALIA, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain

    Baterdene Batmunkh, José Antonio Chica Paez, Sergio Gil Lopez, Maider Arana Bollar, Oihana Jauregi Zorzano & Andoni Aranguren Ubierna

  2. Computational Intelligent Group, University of Basque Country (UPV/EHU), Leioa, Spain

    Manuel Graña & J. David Nuñez-Gonzalez

Authors
  1. Baterdene Batmunkh
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  2. José Antonio Chica Paez
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  3. Sergio Gil Lopez
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  4. Maider Arana Bollar
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  5. Oihana Jauregi Zorzano
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  6. Andoni Aranguren Ubierna
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  7. Manuel Graña
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  8. J. David Nuñez-Gonzalez
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Corresponding author

Correspondence to J. David Nuñez-Gonzalez .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of León, León, Spain

    Hilde Pérez García

  3. Mechanical Engineering Department, University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martinez-de-Pison

  4. Inteligencia Artificial, University of Oviedo, A Coruña, La Coruña, Spain

    José Ramón Villar Flecha

  5. Data Science and Big Data Lab, Pablo de Olavide University, Sevilla, Spain

    Alicia Troncoso Lora

  6. University of Oviedo, Oviedo, Spain

    Enrique A. de la Cal

  7. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  8. School of engineering, Pablo Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  9. DIGIP, University of Bergamo, Dalmine, Bergamo, Italy

    Giuseppe Psaila

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

    Héctor Quintián

  11. Department of Computing Science, University of Salamanca, Salamanca, Spain

    Emilio S. Corchado Rodriguez

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Batmunkh, B. et al. (2023). First Steps Predicting Execution of Civil Works from Georeferenced Infrastructure Data. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_19

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