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^*

^*Autor correspondiente de este trabajo

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	17th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2022, Proceedings
Editores	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
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	197-207
Número de páginas	11
ISBN (versión impresa)	9783031180491
DOI	https://doi.org/10.1007/978-3-031-18050-7_19
Estado	Publicada - 2023
Evento	17th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2022 - Salamanca, Espana Duración: 5 sept 2022 → 7 sept 2022

Serie de la publicación

Nombre	Lecture Notes in Networks and Systems
Volumen	531 LNNS
ISSN (versión impresa)	2367-3370
ISSN (versión digital)	2367-3389

Conferencia

Conferencia	17th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2022
País/Territorio	Espana
Ciudad	Salamanca
Período	5/09/22 → 7/09/22

Acceder al documento

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

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Enlace a la publicación en Scopus

Citar esto

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. En 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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title = "First Steps Predicting Execution of Civil Works from Georeferenced Infrastructure Data",

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}",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 17th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2022 ; Conference date: 05-09-2022 Through 07-09-2022",

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editor = "{Garc{\'i}a Bringas}, Pablo and {P{\'e}rez Garc{\'i}a}, Hilde and Martinez-de-Pison, {Francisco Javier} and {Villar Flecha}, {Jos{\'e} Ram{\'o}n} and {Troncoso Lora}, Alicia and {de la Cal}, {Enrique A.} and Alvaro Herrero and {Mart{\'i}nez {\'A}lvarez}, Francisco and Giuseppe Psaila and H{\'e}ctor Quinti{\'a}n and {Corchado Rodriguez}, {Emilio S.}",

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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. En 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, Espana, 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).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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

AU - Batmunkh, Baterdene

AU - Paez, José Antonio Chica

AU - Lopez, Sergio Gil

AU - Bollar, Maider Arana

AU - Zorzano, Oihana Jauregi

AU - Ubierna, Andoni Aranguren

AU - Graña, Manuel

AU - Nuñez-Gonzalez, J. David

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Y1 - 2023

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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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U2 - 10.1007/978-3-031-18050-7_19

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

M3 - Conference contribution

AN - SCOPUS:85141716228

SN - 9783031180491

T3 - Lecture Notes in Networks and Systems

SP - 197

EP - 207

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

A2 - Troncoso Lora, Alicia

A2 - de la Cal, Enrique A.

A2 - Herrero, Alvaro

A2 - Martínez Álvarez, Francisco

A2 - Psaila, Giuseppe

A2 - Quintián, Héctor

A2 - Corchado Rodriguez, Emilio S.

PB - Springer Science and Business Media Deutschland GmbH

T2 - 17th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2022

Y2 - 5 September 2022 through 7 September 2022

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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. En 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, editores, 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