Skip to main content
SpringerLink
Log in

Machine Lerning for the Analysis of Vegetation in the Heritage City of Salamanca

  • Conference paper
  • First Online:
Highlights in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection (PAAMS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1678))

Abstract

Pollution in cities has emerged as one of the main concerns of the citizens who live there. Cities, increasingly overcrowded, are suffering the effects of Climate Change. However, they can also play a key role in mitigating these consequences thanks to the new Smart Cities, based on IoT technologies, Big Data tools and Artificial Intelligence techniques such as Machine Learning. This article presents a successful case study carried out in the world heritage city of Salamanca in which a platform has been used for the management, analysis and visualisation of the data produced and on which unsupervised machine learning techniques have been applied through clustering (K-means) and supervised through K-Nearest Neighbors (K-NN). The results have proved vital in directing and explaining present and future environmental actions in the city of Salamanca.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Arroyo, Á., Corchado, E., Tricio, V.: Soft computing models to analyze atmospheric pollution issues. Log. J. IGPL 20(4), 699–711 (2012)

    Article  MathSciNet  Google Scholar 

  2. Betts, R.: Technological solutions to mitigating climate change. In: Bandh, S.A. (ed.) Climate Change, pp. 329–368. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-86290-9_19

    Chapter  Google Scholar 

  3. Briones, A.G., et al.: Use of gamification techniques to encourage garbage recycling. A smart city approach. In: Uden, L., Hadzima, B., Ting, I.-H. (eds.) KMO 2018. CCIS, vol. 877, pp. 674–685. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95204-8_56

    Chapter  Google Scholar 

  4. to Bühne, H.S., Tobias, J.A., Durant, S.M., Pettorelli, N.: Improving predictions of climate change-land use change interactions. Trends Ecol. Evol. 36(1), 29–38 (2021)

    Google Scholar 

  5. Bushnell, J., Peterman, C., Wolfram, C.: Local solutions to global problems: climate change policies and regulatory jurisdiction. Rev. Environ. Econ. Policy (2020)

    Google Scholar 

  6. Carvalho, M., Melo-Gonçalves, P., Teixeira, J., Rocha, A.: Regionalization of Europe based on a k-means cluster analysis of the climate change of temperatures and precipitation. Phys. Chem. Earth Parts A/B/C 94, 22–28 (2016)

    Article  Google Scholar 

  7. Chamoso, P., González-Briones, A., De La Prieta, F., Venyagamoorthy, G.K., Corchado, J.M.: Smart city as a distributed platform: toward a system for citizen-oriented management. Comput. Commun. 152, 323–332 (2020)

    Article  Google Scholar 

  8. Corchado, J.M.: Blockchain and its applications on edge computing, Industry 4.0, IoT and smart cities. Dieleman, S (2014)

    Google Scholar 

  9. Corchado, J.M., et al.: Deepint.net: a rapid deployment platform for smart territories. Sensors 21(1), 236 (2021)

    Google Scholar 

  10. Corchado, J.M., Pinto-Santos, F., Aghmou, O., Trabelsi, S.: Intelligent development of smart cities: deepint.net case studies. In: Corchado, J.M., Trabelsi, S. (eds.) SSCTIC 2021. LNNS, vol. 253, pp. 211–225. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-78901-5_19

    Chapter  Google Scholar 

  11. Corchado, J.M., Trabelsi, S.: Advances in sustainable smart cities and territories (2022)

    Google Scholar 

  12. Corchado, J.M., et al.: Smart buildings (2021)

    Google Scholar 

  13. Corchado, J.M.: Technologies for sustainable consumption - researchgate.net (2021). www.researchgate.net/profile/Juan_Rodriguez331/publication/353755163_Technologies_for_sustainable_consumption/links/610ea9491e95fe241abaae5e/Technologies-for-sustainable-consumption.pdf

  14. Corchado Rodríguez, J.M., et al.: DeepTech-AI-IoT in Smart Cities (2021)

    Google Scholar 

  15. Deilami, K., Kamruzzaman, M., Liu, Y.: Urban heat island effect: a systematic review of spatio-temporal factors, data, methods, and mitigation measures. Int. J. Appl. Earth Obs. Geoinf. 67, 30–42 (2018)

    Google Scholar 

  16. Dettori, M., et al.: Environmental risks perception among citizens living near industrial plants: a cross-sectional study. Int. J. Environ. Res. Public Health 17(13), 4870 (2020)

    Article  Google Scholar 

  17. Garcia-Retuerta, D., Chamoso, P., Hernández, G., Guzmán, A.S.R., Yigitcanlar, T., Corchado, J.M.: An efficient management platform for developing smart cities: solution for real-time and future crowd detection. Electronics 10(7), 765 (2021)

    Article  Google Scholar 

  18. Giannico, V., Spano, G., Elia, M., D’Este, M., Sanesi, G., Lafortezza, R.: Green spaces, quality of life, and citizen perception in European cities. Environ. Res. 196, 110922 (2021)

    Google Scholar 

  19. Hassani, H., Huang, X., Silva, E.: Big data and climate change. Big Data Cogn. Comput. 3(1), 12 (2019)

    Article  Google Scholar 

  20. Ingle, H.E., Mikulewicz, M.: Mental health and climate change: tackling invisible injustice. Lancet Planetary Health 4(4), e128–e130 (2020)

    Article  Google Scholar 

  21. Matheus, R., Janssen, M., Maheshwari, D.: Data science empowering the public: data-driven dashboards for transparent and accountable decision-making in smart cities. Gov. Inf. Q. 37(3), 101284 (2020)

    Google Scholar 

  22. Mi, Z., et al.: Cities: the core of climate change mitigation. J. Clean. Prod. 207, 582–589 (2019)

    Article  Google Scholar 

  23. Milojevic-Dupont, N., Creutzig, F.: Machine learning for geographically differentiated climate change mitigation in urban areas. Sustain. Urban Areas 64, 102526 (2021)

    Google Scholar 

  24. Pérez-Pons, M.E., Parra-Domínguez, J., Chamoso, P., Plaza, M., Alonso, R.: Efficiency, profitability and productivity: technological applications in the agricultural sector. ADCAIJ: Adv. Distrib. Comput. Artif. Intell. J. 9(4) (2020)

    Google Scholar 

  25. Vía de la Plata, L.: Proyecto life vía de la plata (2022). www.lifeviadelaplata.com/

  26. Querejeta, M.U., Alonso, R.S.: Modeling air quality and cancer incidences in proximity to hazardous waste and incineration treatment areas. In: Second International Workshop on Data Engineering and Analytics (WDEA 2019), pp. 108–122 (2019)

    Google Scholar 

  27. O’Dea, S.: Global IoT connections data volume 2019 and 2025 (2020). www.statista.com/statistics/1017863/worldwide-iot-connected-devices-data-size/

  28. Sittón-Candanedo, I., Alonso, R.S., Corchado, J.M., Rodríguez-González, S., Casado-Vara, R.: A review of edge computing reference architectures and a new global edge proposal. Futur. Gener. Comput. Syst. 99, 278–294 (2019)

    Article  Google Scholar 

  29. Sittón-Candanedo, I., Alonso, R.S., Múñoz, L., Rodríguez-González, S.: Arquitecturas de referencia edge computing para la industria 4.0: una revisión. In: Memorias de Congresos UTP, pp. 16–23 (2019)

    Google Scholar 

  30. European Union: Regulation (EU) no 1293/2013 of the European parliament and of the council (2013). https://eur-lex.europa.eu/legal-content/ES/TXT/?uri=CELEX3A32013R1293

  31. European Union: Copernicus (2022). https://www.copernicus.eu

  32. European Union: Normalized difference vegetation index (2022). https://land.copernicus.eu/global/products/ndvi

  33. Wang, X., Wang, J.: Using clustering methods in geospatial information systems. Geomatica 64(3), 347–361 (2010)

    Google Scholar 

  34. Yang, Z., Shen, Y., Li, J., Jiang, H., Zhao, L.: A clustering method for inter-annual NDVI time series. Remote Sens. Lett. 12(8), 819–826 (2021)

    Article  Google Scholar 

  35. Yigitcanlar, T., Butler, L., Windle, E., Desouza, K.C., Mehmood, R., Corchado, J.M.: Can building “artificially intelligent cities’’ safeguard humanity from natural disasters, pandemics, and other catastrophes? An urban scholar’s perspective. Sensors 20(10), 2988 (2020)

    Article  Google Scholar 

  36. Yigitcanlar, T., Corchado, J.M., Mehmood, R., Li, R.Y.M., Mossberger, K., Desouza, K.: Responsible urban innovation with local government artificial intelligence (AI): a conceptual framework and research agenda. J. Open Innov.: Technol. Market Complex. 7(1), 71 (2021)

    Article  Google Scholar 

  37. Yigitcanlar, T., Mehmood, R., Corchado, J.M.: Green artificial intelligence: towards an efficient, sustainable and equitable technology for smart cities and futures. Sustainability 13(16), 8952 (2021)

    Article  Google Scholar 

  38. Zhongming, Z., Wei, L., et al.: Urban adaptation to climate change in Europe 2016—transforming cities in a changing climate (2016)

    Google Scholar 

Download references

Acknowledgements

This work has been partially supported by the LIFE program of the European Commission (LIFE Vía de la Plata project: LIFE19 CCA/ES/001188).

Author information

Authors and Affiliations

  1. BISITE Research Group, University of Salamanca, Edificio Multiusos I+D+i, Calle Espejo 2, 37007, Salamanca, Spain

    Raúl López-Blanco, Ricardo S. Alonso, Javier Parra-Domínguez, Angélica González-Arrieta, Jose A. Flores-Villarejo, Miguel Á. Fuertes-Prieto, Román Andrés-Bondía & Javier Prieto

  2. AIR Institute - Deep Tech Lab, IoT Digital Innovation Hub, Salamanca, Spain

    Ricardo S. Alonso

  3. Ayuntamiento de Salamanca, Plaza Mayor, 1, 37001, Salamnca, Spain

    Beatriz Diosdado-Calvo

Authors
  1. Raúl López-Blanco
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ricardo S. Alonso
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Javier Parra-Domínguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Angélica González-Arrieta
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jose A. Flores-Villarejo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Miguel Á. Fuertes-Prieto
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Beatriz Diosdado-Calvo
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Román Andrés-Bondía
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Javier Prieto
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Raúl López-Blanco .

Editor information

Editors and Affiliations

  1. University of Salamanca, Salamanca, Spain

    Alfonso González-Briones

  2. Institute of Engineering - Polytechnic of Porto, Porto, Portugal

    Ana Almeida

  3. Universidad Rey Juan Carlos, Madrid, Spain

    Alberto Fernandez

  4. German International University, Cairo, Egypt

    Alia El Bolock

  5. University of Minho, Braga, Portugal

    Dalila Durães

  6. Universitat Politècnica de València, Valencia, Spain

    Jaume Jordán

  7. National Laboratory of Energy and Geology, Amadora, Portugal

    Fernando Lopes

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

López-Blanco, R. et al. (2022). Machine Lerning for the Analysis of Vegetation in the Heritage City of Salamanca. In: González-Briones, A., et al. Highlights in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection. PAAMS 2022. Communications in Computer and Information Science, vol 1678. Springer, Cham. https://doi.org/10.1007/978-3-031-18697-4_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-18697-4_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18696-7

  • Online ISBN: 978-3-031-18697-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation