First Steps Predicting Execution of Civil Works from Georeferenced Infrastructure Data

Baterdene Batmunkh; José Antonio Chica Paez; Sergio Gil Lopez; Maider Arana Bollar; Oihana Jauregi Zorzano; Andoni Aranguren Ubierna; Manuel Graña; J. David Nuñez-Gonzalez

doi:10.1007/978-3-031-18050-7_19

First Steps Predicting Execution of Civil Works from Georeferenced Infrastructure Data

Baterdene Batmunkh, José Antonio Chica Paez, Sergio Gil Lopez, Maider Arana Bollar, Oihana Jauregi Zorzano, Andoni Aranguren Ubierna, Manuel Graña, J. David Nuñez-Gonzalez^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	17th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2022, Proceedings
Editors	Pablo García Bringas, Hilde Pérez García, Francisco Javier Martinez-de-Pison, José Ramón Villar Flecha, Alicia Troncoso Lora, Enrique A. de la Cal, Alvaro Herrero, Francisco Martínez Álvarez, Giuseppe Psaila, Héctor Quintián, Emilio S. Corchado Rodriguez
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	197-207
Number of pages	11
ISBN (Print)	9783031180491
DOIs	https://doi.org/10.1007/978-3-031-18050-7_19
Publication status	Published - 2023
Event	17th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2022 - Salamanca, Spain Duration: 5 Sept 2022 → 7 Sept 2022

Publication series

Name	Lecture Notes in Networks and Systems
Volume	531 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	17th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2022
Country/Territory	Spain
City	Salamanca
Period	5/09/22 → 7/09/22

Access to Document

10.1007/978-3-031-18050-7_19

Cite this

Batmunkh, B., Paez, J. A. C., Lopez, S. G., Bollar, M. A., Zorzano, O. J., Ubierna, A. A., Graña, M., & Nuñez-Gonzalez, J. D. (2023). First Steps Predicting Execution of Civil Works from Georeferenced Infrastructure Data. In P. García Bringas, H. Pérez García, F. J. Martinez-de-Pison, J. R. Villar Flecha, A. Troncoso Lora, E. A. de la Cal, A. Herrero, F. Martínez Álvarez, G. Psaila, H. Quintián, & E. S. Corchado Rodriguez (Eds.), 17th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2022, Proceedings (pp. 197-207). (Lecture Notes in Networks and Systems; Vol. 531 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-18050-7_19

Batmunkh, Baterdene ; Paez, José Antonio Chica ; Lopez, Sergio Gil et al. / First Steps Predicting Execution of Civil Works from Georeferenced Infrastructure Data. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2022, Proceedings. editor / Pablo García Bringas ; Hilde Pérez García ; Francisco Javier Martinez-de-Pison ; José Ramón Villar Flecha ; Alicia Troncoso Lora ; Enrique A. de la Cal ; Alvaro Herrero ; Francisco Martínez Álvarez ; Giuseppe Psaila ; Héctor Quintián ; Emilio S. Corchado Rodriguez. Springer Science and Business Media Deutschland GmbH, 2023. pp. 197-207 (Lecture Notes in Networks and Systems).

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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.",

author = "Baterdene Batmunkh and Paez, {Jos{\'e} Antonio Chica} and Lopez, {Sergio Gil} and Bollar, {Maider Arana} and Zorzano, {Oihana Jauregi} and Ubierna, {Andoni Aranguren} and Manuel Gra{\~n}a and Nu{\~n}ez-Gonzalez, {J. David}",

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doi = "10.1007/978-3-031-18050-7_19",

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Batmunkh, B, Paez, JAC , Lopez, SG , Bollar, MA , Zorzano, OJ , Ubierna, AA, Graña, M & Nuñez-Gonzalez, JD 2023, First Steps Predicting Execution of Civil Works from Georeferenced Infrastructure Data. in P García Bringas, H Pérez García, FJ Martinez-de-Pison, JR Villar Flecha, A Troncoso Lora, EA de la Cal, A Herrero, F Martínez Álvarez, G Psaila, H Quintián & ES Corchado Rodriguez (eds), 17th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2022, Proceedings. Lecture Notes in Networks and Systems, vol. 531 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 197-207, 17th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2022, Salamanca, Spain, 5/09/22. https://doi.org/10.1007/978-3-031-18050-7_19

First Steps Predicting Execution of Civil Works from Georeferenced Infrastructure Data. / Batmunkh, Baterdene; Paez, José Antonio Chica ; Lopez, Sergio Gil et al.
17th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2022, Proceedings. ed. / Pablo García Bringas; Hilde Pérez García; Francisco Javier Martinez-de-Pison; José Ramón Villar Flecha; Alicia Troncoso Lora; Enrique A. de la Cal; Alvaro Herrero; Francisco Martínez Álvarez; Giuseppe Psaila; Héctor Quintián; Emilio S. Corchado Rodriguez. Springer Science and Business Media Deutschland GmbH, 2023. p. 197-207 (Lecture Notes in Networks and Systems; Vol. 531 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - First Steps Predicting Execution of Civil Works from Georeferenced Infrastructure Data

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AU - Paez, José Antonio Chica

AU - Lopez, Sergio Gil

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AU - Zorzano, Oihana Jauregi

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AU - Graña, Manuel

AU - Nuñez-Gonzalez, J. David

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AB - 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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DO - 10.1007/978-3-031-18050-7_19

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SN - 9783031180491

T3 - Lecture Notes in Networks and Systems

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BT - 17th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2022, Proceedings

A2 - García Bringas, Pablo

A2 - Pérez García, Hilde

A2 - Martinez-de-Pison, Francisco Javier

A2 - Villar Flecha, José Ramón

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A2 - Psaila, Giuseppe

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Batmunkh B, Paez JAC , Lopez SG , Bollar MA , Zorzano OJ , Ubierna AA et al. First Steps Predicting Execution of Civil Works from Georeferenced Infrastructure Data. In García Bringas P, Pérez García H, Martinez-de-Pison FJ, Villar Flecha JR, Troncoso Lora A, de la Cal EA, Herrero A, Martínez Álvarez F, Psaila G, Quintián H, Corchado Rodriguez ES, editors, 17th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 197-207. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-18050-7_19