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GAEA: A Country-Scale Geospatial Environmental Modelling Tool: Towards a Digital Twin for Real Estate

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Advances and New Trends in Environmental Informatics 2023 (ENVIROINFO 2023)

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Abstract

Monitoring the physical and artificial environment at large-scale is crucial for approaching significant problems such as climate change, biodiversity loss, and sustainable urban growth. Towards this direction, GAEA is a novel AI-empowered geospatial online tool, designed to facilitate country-scale environmental monitoring, modelling, analytics, and geo-visualizations, providing valuable insights in the geographical region of the country of Cyprus, with some focus on the real estate application domain. This paper presents the design and development of GAEA, the needs and requirements it addresses, the environmental services it offers, its implementation details and main features, and an evaluation and discussion of its perspectives and overall potential. GAEA offers a user-friendly web interface that allows users to interact with a wide range of services, including land use monitoring, climate information, geohazard, and proximity analysis. GAEA is an important milestone and real-world demonstration of the vision of creating a country-scale environmental digital twin that allows informed decisions in land use assessment, climate analysis, and disaster management.

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Notes

  1. 1.

    SuPerWorld Geospatial API: https://superworld.cyens.org.cy/product1.html.

References

  1. Yang, L., Driscol, J., Sarigai, S., Wu, Q., Chen, H., Lippitt, C.D.: Google earth engine and artificial intelligence (ai): a comprehensive review. Remote Sens. 14(14), 3253 (2022)

    Article  Google Scholar 

  2. Palaiologou, P., Kalabokidis, K., Day, M.A., Ager, A.A., Galatsidas, S., Papalampros, L.: Modelling fire behavior to assess community exposure in europe: combining open data and geospatial analysis. ISPRS Int. J. Geo-Inf. 11(3), 198 (2022)

    Article  Google Scholar 

  3. Verma, S., Gupta, S., Shin, H., Panigrahi, A., Goswami, S., Pardeshi, S., Exe, N., Dutta, U., Joshi, T.R., Bhojwani, N.: Geoengine: a platform for production-ready geospatial research. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21,416–21,424 (2022)

    Google Scholar 

  4. Zhang, X., Ma, C., Yang, G.: City appearance environment management system based on webgis. In: Third International Conference on Computer Science and Communication Technology (ICCSCT 2022), vol. 12506, pp. 1389–1398. SPIE (2022)

    Google Scholar 

  5. Morales, C., Díaz, A.S.-P., Dionisio, D., Guarnieri, L., Marchi, G., Maniatis, D., Mollicone, D.: Earth map: a novel tool for fast performance of advanced land monitoring and climate assessment. J. Remote Sens. 3, 0003 (2023)

    Article  Google Scholar 

  6. Ignatius, M., Wong, N., Martin, M., Chen, S.: Virtual singapore integration with energy simulation and canopy modelling for climate assessment. In: IOP Conference Series: Earth and Environmental Science, vol. 294, no. 1, p. 012018. IOP Publishing (2019)

    Google Scholar 

  7. Lee, A., Lee, K.-W., Kim, K.-H., Shin, S.-W.: A geospatial platform to manage large-scale individual mobility for an urban digital twin platform. Remote Sens. 14(3), 723 (2022)

    Article  Google Scholar 

  8. Ghita, M., Siham, B., Hicham, M., Hafid, G.: Artificial and geospatial intelligence driven digital twins’ architecture development against the worldwide twin crisis caused by covid-19. Geospatial Intelligence: Applications and Future Trends, pp. 79–104. Springer, Cham (2022)

    Google Scholar 

  9. Rtree: https://pypi.org/project/Rtree/. Last accessed 10 June 2023

  10. Google Earth: https://earth.google.com/. Last accessed 10 Sept 2022

  11. Label Studio - Annotation Tool: https://github.com/heartexlabs/label-studio. last accessed 10 June 2023

  12. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18, pp. 234–241. Springer (2015)

    Google Scholar 

  13. Shah, A., Kadam, E., Shah, H., Shinde, S., Shingade, S.: Deep residual networks with exponential linear unit. In: Proceedings of the Third International Symposium on Computer Vision and the Internet, ser. VisionNet’16. New York, NY, USA: Association for Computing Machinery, pp. 59–65. (2016) [Online]. Available: https://doi.org/10.1145/2983402.2983406

  14. PlanetScope: https://earth.esa.int/eogateway/missions/planetscope. Last accessed 10 Sept 2022

  15. Chicco, D.: Siamese neural networks: an overview. Artificial Neural Networks, pp. 73–94. Humana, New York (2021)

    Google Scholar 

  16. Lv, Z., Shi, W., Zhou, X., Benediktsson, J.A.: Semi-automatic system for land cover change detection using bi-temporal remote sensing images. Remote Sens. 9(11), 1112 (2017)

    Article  Google Scholar 

  17. Lang, N., Jetz, W., Schindler, K., Wegner, J.D.: A high-resolution canopy height model of the earth. Nat. Ecol. Evol. 7(11), 1778–1789 (2023). https://doi.org/10.1038/s41559-023-02206-6. Epub 2023 Sep 28. PMID: 37770546; PMCID: PMC10627820

  18. Palazzo, F. Šmejkalová, T., Castro-Gomez, M., Rémondière, S., Scarda, B., Bonneval, B., Gilles, C., Guzzonato, E., Mora, B.: Rus: a new expert service for sentinel users. In: Proceedings, vol. 2, no. 7 (2018) [Online]. Available: https://www.mdpi.com/2504-3900/2/7/369

  19. ESDAC - European Commission: https://esdac.jrc.ec.europa.eu/. Last accessed 10 June 2023

  20. Landsat Collection: 25-08-2021 , Landsat7, 08 2021

    Google Scholar 

  21. Blue Flag: https://www.blueflag.global. Last accessed 10 June 2022

  22. Google for Developers: https://developers.google.com/maps/documentation/places/web-service/overview. Last accessed 10 June 2023

  23. CHIRPS Rainfall Dataset: https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_DAILY. Last accessed 10 Jan 2023

  24. Guillera-Arroita, G., Lahoz-Monfort, J.J., Elith, J., Gordon, A., Kujala, H., Lentini, P.E., McCarthy, M.A., Tingley, R., Wintle, B.A.: Is my species distribution model fit for purpose? matching data and models to applications. Glob. Ecol. Biogeogr. 24(3), 276–292 (2015)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. CYENS Center of Excellence, Nicosia, Cyprus

    Asfa Jamil, Chirag Padubidri, Savvas Karatsiolis, Indrajit Kalita, Aytac Guley & Andreas Kamilaris

  2. Cyprus University of Technology, Limassol, Cyprus

    Chirag Padubidri

  3. University of Twente, Enschede, The Netherlands

    Andreas Kamilaris

Authors
  1. Asfa Jamil
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  2. Chirag Padubidri
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  3. Savvas Karatsiolis
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  4. Indrajit Kalita
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  5. Aytac Guley
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  6. Andreas Kamilaris
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Corresponding author

Correspondence to Asfa Jamil .

Editor information

Editors and Affiliations

  1. HTW Berlin, University of Applied Sciences, Berlin, Germany

    Volker Wohlgemuth

  2. Ludwig-Maximilians-Universität München, München, Germany

    Dieter Kranzlmüller

  3. Leibniz Supercomputing Centre, Garching, Germany

    Maximilian Höb

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Jamil, A., Padubidri, C., Karatsiolis, S., Kalita, I., Guley, A., Kamilaris, A. (2024). GAEA: A Country-Scale Geospatial Environmental Modelling Tool: Towards a Digital Twin for Real Estate. In: Wohlgemuth, V., Kranzlmüller, D., Höb, M. (eds) Advances and New Trends in Environmental Informatics 2023. ENVIROINFO 2023. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-031-46902-2_10

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