Skip to main content

Integrated Methods for Cultural Heritage Risk Assessment: Google Earth Engine, Spatial Analysis, Machine Learning

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

Abstract

Cultural heritage risk assessment is an important task. Usually cultural heritage inside the consolidated city is more protected than cultural heritage spread over the territory. In this paper a method is proposed that integrates different technologies and platforms: from Google Earth Engine (GEE) and machine learning to desktop GIS and spatial analysis. The aim is to map the different vulnerability and hazard layers that constitute the cultural heritage risk map of the rural area in the Matera Municipality.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Serra, M., D’Agostino, S.: Archeologia Preventiva. Agenzia Magna Grecia, Albanella (SA) (2010)

    Google Scholar 

  2. Cruden, D.M., Varnes, D.J.: Landslide types and processes. In: Turner, A.K., Shuster, R.L. (eds.) Landslides: Investigation and Mitigation, pp. 36–75. National Academies Press, Washington DC (1996)

    Google Scholar 

  3. Biscione, M., Danese, M., Masini, N.: A framework for cultural heritage management and research: the cancellara case study. J. Maps 14, 576–582 (2018)

    Article  Google Scholar 

  4. Danese, M., Gioia, D., Biscione, M., Masini, N.: Spatial methods for archaeological flood risk: the case study of the neolithic sites in the Apulia Region (Southern Italy). In: Murgante, B., et al. (eds.) ICCSA 2014. LNCS, vol. 8579, pp. 423–439. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09144-0_29

    Chapter  Google Scholar 

  5. Danese, M., Masini, N., Biscione, M., Lasaponara, R.: Predictive modeling for preventive archaeology: overview and case study. Cent. Eur. J. Geosci. 6(1), 42–55 (2014). https://doi.org/10.2478/s13533-012-0160-5

    Article  Google Scholar 

  6. Yan, L., et al.: Towards an operative predictive model for the songshan area during the yangshao period. ISPRS Int. J. Geo-Inf. 10, 217 (2021)

    Google Scholar 

  7. https://blog.google/outreach-initiatives/sustainability/introducing-google-earth-engine/

  8. Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., Brisco, B.: Google Earth Engine for geo-big data applications: a meta-analysis and systematic review. ISPRS J. Photogramm. Remote. Sens. 164, 152–170 (2020)

    Article  Google Scholar 

  9. Moore, R.T., Hansen, M.C.: Google Earth Engine: a new cloud-computing platform for global-scale earth observation data and analysis. In: AGU Fall Meeting Abstracts (2011)

    Google Scholar 

  10. Ravanelli, R., et al.: Monitoring the impact of land cover change on surface urban heat island through google earth engine: proposal of a global methodology, first applications and problems. Remote Sens. 10, 1488 (2018)

    Google Scholar 

  11. Mutanga, O., Kumar, L.: Google Earth Engine applications. Remote Sens. 11 (2019)

    Google Scholar 

  12. Patel, N.N., et al.: Multitemporal settlement and population mapping from Landsat using Google Earth Engine. Int. J. Appl. Earth Obs. Geoinf. 35, 199–208 (2015)

    Article  Google Scholar 

  13. Huntington, J.L., et al.: Climate engine: cloud computing and visualization of climate and remote sensing data for advanced natural resource monitoring and process understanding. Bull. Am. Meteor. Soc. 98, 2397–2410 (2017)

    Article  Google Scholar 

  14. Agapiou, A.: Remote sensing heritage in a petabyte-scale: satellite data and heritage Earth Engine© applications. Int. J. Digit. Earth 10, 85–102 (2017)

    Article  Google Scholar 

  15. Kleinberg, E.M.: On the algorithmic implementation of stochastic discrimination. IEEE Trans. Pattern Anal. Mach. Intell. 22, 473–490 (2000)

    Article  Google Scholar 

  16. Kleinberg, E.M.: An overtraining-resistant stochastic modeling method for pattern recognition. Ann. Stat. 24, 2319–2349 (1996)

    Article  MathSciNet  Google Scholar 

  17. Belgiu, M., Drăguţ, L.: Random forest in remote sensing: a review of applications and future directions. ISPRS J. Photogramm. Remote. Sens. 114, 24–31 (2016)

    Article  Google Scholar 

  18. Sheykhmousa, M., Mahdianpari, M., Ghanbari, H., Mohammadimanesh, F., Ghamisi, P., Homayouni, S.: Support vector machine versus random forest for remote sensing image classification: a meta-analysis and systematic review. IEEE J. Selected Topics Appl. Earth Obs. Remote Sens. 13, 6308–6325 (2020)

    Article  Google Scholar 

  19. Cao, G.Y., Chen, G., Pang, L.H., Zheng, X.Y., Nilsson, S.: Urban growth in China: past, prospect, and its impacts. Popul. Environ. 33, 137–160 (2012)

    Article  Google Scholar 

  20. Demers, M.: GIS modeling in raster (2001)

    Google Scholar 

  21. Tomlin, C.D.: GIS and Cartographic Modeling. Esri Press, Redlands, California (2013)

    Google Scholar 

  22. Beneduce, P., Festa, V., Francioso, R., Schiattarella, M., Tropeano, M.: Conflicting drainage patterns in the Matera Horst Area, Southern Italy. Phys. Chem. Earth 29, 717–724 (2004)

    Article  Google Scholar 

  23. Tropeano, M., Sabato, L., Pieri, P.: Filling and cannibalization of a foredeep: the Bradanic Trough Southern Italy. Geol. Soc. Spec. Publ. 191, 55–79 (2002)

    Article  Google Scholar 

  24. Gioia, D., Sabato, L., Spalluto, L., Tropeano, M.: Fluvial landforms in relation to the geological setting in the “Murge Basse” karst of Apulia (Bari Metropolitan Area, Southern Italy). J. Maps 7, 148–155 (2011)

    Google Scholar 

  25. Teofilo, G., Gioia, D., Spalluto, L.: Integrated geomorphological and geospatial analysis for mapping fluvial landforms in Murge basse karst of Apulia (Southern Italy). Geosciences (Switzerland) 9, 418 (2019)

    Google Scholar 

  26. Gioia, D., Schiattarella, M., Giano, S.: Right-angle pattern of minor fluvial networks from the ionian terraced belt, Southern Italy: passive structural control or foreland bending? Geosciences 8, 331 (2018)

    Article  Google Scholar 

  27. Pérez-Hernández, E., Peña-Alonso, C., Hernández-Calvento, L.: Assessing lost cultural heritage. a case study of the eastern coast of Las Palmas de Gran Canaria city (Spain). L. Use Policy 96, 104697 (2020)

    Google Scholar 

  28. de Noronha Vaz, E., Cabral, P., Caetano, M., Nijkamp, P.: Urban heritage endangerment at the interface of future cities and past heritage: a spatial vulnerability assessment. Serie Research Memoranda 0036, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics (2011)

    Google Scholar 

  29. Fry, G.L.A., Skar, B., Jerpåsen, G., Bakkestuen, V., Erikstad, L.: Locating archaeological sites in the landscape: a hierarchical approach based on landscape indicators. Landsc. Urban Plan. 67, 97–107 (2004)

    Article  Google Scholar 

  30. Agapiou, A., et al.: Impact of Urban sprawl to cultural heritage monuments: the case study of paphos area in Cyprus. J. Cult. Herit. 16, 671–680 (2015)

    Article  Google Scholar 

  31. Congalton, R.: Putting the Map Back in Map Accuracy Assessment, pp. 1–11 (2004)

    Google Scholar 

  32. Lazzari, M., Gioia, D., Anzidei, B.: Landslide inventory of the Basilicata region (Southern Italy). J. Maps 14, 348–356 (2018)

    Article  Google Scholar 

  33. Mitasova, H., Hofierka, J., Zlocha, M., Iverson, L.R.: Modelling topographic potential for erosion and deposition using GIS. Int. J. Geogr. Inf. Syst. 10, 629–641 (1996)

    Article  Google Scholar 

  34. Renard, K.G.: Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). United States Government Printing (1997)

    Google Scholar 

  35. Capolongo, D., Diodato, N., Mannaerts, C.M., Piccarreta, M., Strobl, R.O.: Analyzing temporal changes in climate erosivity using a simplified rainfall erosivity model in Basilicata (Southern Italy). J. Hydrol. 356, 119–130 (2008)

    Article  Google Scholar 

  36. Büttner, G.: CORINE land cover and land cover change products. In: Manakos, I., Braun, M. (eds.) Land Use and Land Cover Mapping in Europe. RSDIP, vol. 18, pp. 55–74. Springer, Dordrecht (2014). https://doi.org/10.1007/978-94-007-7969-3_5

    Chapter  Google Scholar 

  37. Renard, K.G., Foster, G.R., Weesies, G.A., Porter, J.P.: RUSLE: revised universal soil loss equation. J. Soil Water Conserv. 46, 30–33 (1991)

    Google Scholar 

Download references

Acknowledgement

Authors want to thank Guido Ceccarini of the European Commission Joint Research Centre, who freely shared the script which GEE elaboration for urban growth analysis are inspired.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Maria Danese .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Danese, M., Gioia, D., Biscione, M. (2021). Integrated Methods for Cultural Heritage Risk Assessment: Google Earth Engine, Spatial Analysis, Machine Learning. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12951. Springer, Cham. https://doi.org/10.1007/978-3-030-86970-0_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-86970-0_42

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86969-4

  • Online ISBN: 978-3-030-86970-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics