Satellite Big Data for Archaeological Discovery: A Digital Earth Perspective
Ключевые слова:
Big Data, Remote Sensing, Digital Earth, Archaeology, Archaeological Sensitivity
Аннотация
The advent of modern big data platforms has revolutionized archaeology, enabling the discovery of new sites on a planetary scale. This article overviews how these platforms, within the Digital Earth framework, process and analyze vast amounts of remote sensing data. We explore the capabilities of major publicly accessible platforms in integrating diverse geospatial datasets and employing advanced analytical tools. Through case studies, we illustrate the transformative impact of these technologies on archaeological research, highlighting notable discoveries and the role of collaboration and crowdsourcing. Despite challenges such as data quality and computational costs, future advancements promise to enhance our ability to uncover and understand historical heritage. This work underscores the significant potential of big data platforms in advancing archaeological discoveries and enriching our knowledge of the past through the Digital Earth initiative.
Литература
Agapiou, A. (2017). Remote sensing heritage in a petabyte-scale: Satellite data and heritage Earth Engine© applications. International Journal of Digital Earth, 10(1), 85–102. https://doi.org/10.1080/17538947.2016.1250829
Annoni, A., Nativi, S., Çöltekin, A., Desha, C., Eremchenko, E., Gevaert, C. M., Giuliani, G., Chen, M., Perez-Mora, L., Strobl, J., & Tumampos, S. (2023). Digital Earth: Yesterday, today, and tomorrow. International Journal of Digital Earth, 16(1), 1022–1072. https://doi.org/10.1080/17538947.2023.2187467
ArcGIS Earth App. (2023). [Software]. ESRI. https://www.esri.com/en-us/arcgis/products/arcgis-earth/overview
Caracausi, S., Berruti, G. L. F., Daffara, S., Bertè, D., & Rubat Borel, F. (2018). Use of a GIS predictive model for the identification of high altitude prehistoric human frequentations. Results of the Sessera valley project (Piedmont, Italy). Quaternary International, 490(November 2017), 10–20. https://doi.org/10.1016/j.quaint.2018.05.038
Cieslar, C., & Vasyunin, Y. (2023). Identification of archeological sites in the Peruvian Amazon using satellite remote sensing. Unpublished. https://doi.org/10.13140/RG.2.2.11555.43048
Davis, D. (2021). Theoretical Repositioning of Automated Remote Sensing Archaeology: Shifting from Features to Ephemeral Landscapes. Journal of Computer Applications in Archaeology, 4(1), 94. https://doi.org/10.5334/jcaa.72
Deng, X., Liu, P., Liu, X., Wang, R., Zhang, Y., He, J., & Yao, Y. (2019). Geospatial Big Data: New Paradigm of Remote Sensing Applications. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(10), 3841–3851. https://doi.org/10.1109/JSTARS.2019.2944952
Earth Versions. (2023). [Software]. Google Earth. https://www.google.com/intl/ru/earth/about/versions/
Gore, A. (1998). The Digital Earth: Understanding our planet in the 21st Century. Australian Surveyor, 43(2), 89–91. https://doi.org/10.1080/00050348.1998.10558728
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
Herndon, K. E., Griffin, R., Schroder, W., Murtha, T., Golden, C., Contreras, D. A., Cherrington, E., Wang, L., Bazarsky, A., Kollias, G. V., & Alcover Firpi, O. (2023). Google Earth Engine for archaeologists: An updated look at the progress and promise of remotely sensed big data. Journal of Archaeological Science: Reports, 50, 104094. https://doi.org/10.1016/j.jasrep.2023.104094
Howey, M. C. L., & Burg, M. B. (2017). Assessing the state of archaeological GIS research: Unbinding analyses of past landscapes. Journal of Archaeological Science, 84, 1–9. https://doi.org/10.1016/j.jas.2017.05.002
Howey, M. C. L., Sullivan, F. B., Burg, M. B., & Palace, M. W. (2020). Remotely Sensed Big Data and Iterative Approaches to Cultural Feature Detection and Past Landscape Process Analysis. Journal of Field Archaeology, 45(sup1), S27–S38. https://doi.org/10.1080/00934690.2020.1713435
Kalafatić, H., Klindžić, R. Š., & Šiljeg, B. (2020). Being Enclosed as a Lifestyle: Complex Neolithic Settlements of Eastern Croatia Re-Evaluated through Aerial and Magnetic Survey. Geosciences, 10(10), 384. https://doi.org/10.3390/geosciences10100384
Loukili, Y., Lakhrissi, Y., & Ali, S. E. B. (2022). Geospatial Big Data Platforms: A Comprehensive Review. KN - Journal of Cartography and Geographic Information, 72(4), 293–308. https://doi.org/10.1007/s42489-022-00121-7
Luo, L., Wang, X., Guo, H., Lasaponara, R., Zong, X., Masini, N., Wang, G., Shi, P., Khatteli, H., Chen, F., Tariq, S., Shao, J., Bachagha, N., Yang, R., & Yao, Y. (2019). Airborne and spaceborne remote sensing for archaeological and cultural heritage applications: A review of the century (1907–2017). Remote Sensing of Environment, 232(March), 111280. https://doi.org/10.1016/j.rse.2019.111280
Microsoft Open Source, McFarland, M., Emanuele, R., Morris, D., & Augspurger, T. (2022). microsoft/PlanetaryComputer: October 2022 (2022.10.28) [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.7261897
Orengo, H. A., Conesa, F. C., Garcia-Molsosa, A., Lobo, A., Green, A. S., Madella, M., & Petrie, C. A. (2020). Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data. Proceedings of the National Academy of Sciences, 117(31), 18240–18250. https://doi.org/10.1073/pnas.2005583117
Orengo, H., & Petrie, C. (2017). Large-Scale, Multi-Temporal Remote Sensing of Palaeo-River Networks: A Case Study from Northwest India and its Implications for the Indus Civilisation. Remote Sensing, 9(7), 735. https://doi.org/10.3390/rs9070735
Sentinel Hub. (2023). Sinergise Solutions d.o.o., a Planet Labs Company. https://www.sentinel-hub.com/
Visentin, D., Carrer, F., Fontana, F., Cavulli, F., Cesco Frare, P., Mondini, C., & Pedrotti, A. (2016). Prehistoric landscapes of the Dolomites: Survey data from the highland territory of Cadore (Belluno Dolomites, Northern Italy). Quaternary International, 402, 5–14. https://doi.org/10.1016/j.quaint.2015.10.080
Annoni, A., Nativi, S., Çöltekin, A., Desha, C., Eremchenko, E., Gevaert, C. M., Giuliani, G., Chen, M., Perez-Mora, L., Strobl, J., & Tumampos, S. (2023). Digital Earth: Yesterday, today, and tomorrow. International Journal of Digital Earth, 16(1), 1022–1072. https://doi.org/10.1080/17538947.2023.2187467
ArcGIS Earth App. (2023). [Software]. ESRI. https://www.esri.com/en-us/arcgis/products/arcgis-earth/overview
Caracausi, S., Berruti, G. L. F., Daffara, S., Bertè, D., & Rubat Borel, F. (2018). Use of a GIS predictive model for the identification of high altitude prehistoric human frequentations. Results of the Sessera valley project (Piedmont, Italy). Quaternary International, 490(November 2017), 10–20. https://doi.org/10.1016/j.quaint.2018.05.038
Cieslar, C., & Vasyunin, Y. (2023). Identification of archeological sites in the Peruvian Amazon using satellite remote sensing. Unpublished. https://doi.org/10.13140/RG.2.2.11555.43048
Davis, D. (2021). Theoretical Repositioning of Automated Remote Sensing Archaeology: Shifting from Features to Ephemeral Landscapes. Journal of Computer Applications in Archaeology, 4(1), 94. https://doi.org/10.5334/jcaa.72
Deng, X., Liu, P., Liu, X., Wang, R., Zhang, Y., He, J., & Yao, Y. (2019). Geospatial Big Data: New Paradigm of Remote Sensing Applications. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(10), 3841–3851. https://doi.org/10.1109/JSTARS.2019.2944952
Earth Versions. (2023). [Software]. Google Earth. https://www.google.com/intl/ru/earth/about/versions/
Gore, A. (1998). The Digital Earth: Understanding our planet in the 21st Century. Australian Surveyor, 43(2), 89–91. https://doi.org/10.1080/00050348.1998.10558728
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
Herndon, K. E., Griffin, R., Schroder, W., Murtha, T., Golden, C., Contreras, D. A., Cherrington, E., Wang, L., Bazarsky, A., Kollias, G. V., & Alcover Firpi, O. (2023). Google Earth Engine for archaeologists: An updated look at the progress and promise of remotely sensed big data. Journal of Archaeological Science: Reports, 50, 104094. https://doi.org/10.1016/j.jasrep.2023.104094
Howey, M. C. L., & Burg, M. B. (2017). Assessing the state of archaeological GIS research: Unbinding analyses of past landscapes. Journal of Archaeological Science, 84, 1–9. https://doi.org/10.1016/j.jas.2017.05.002
Howey, M. C. L., Sullivan, F. B., Burg, M. B., & Palace, M. W. (2020). Remotely Sensed Big Data and Iterative Approaches to Cultural Feature Detection and Past Landscape Process Analysis. Journal of Field Archaeology, 45(sup1), S27–S38. https://doi.org/10.1080/00934690.2020.1713435
Kalafatić, H., Klindžić, R. Š., & Šiljeg, B. (2020). Being Enclosed as a Lifestyle: Complex Neolithic Settlements of Eastern Croatia Re-Evaluated through Aerial and Magnetic Survey. Geosciences, 10(10), 384. https://doi.org/10.3390/geosciences10100384
Loukili, Y., Lakhrissi, Y., & Ali, S. E. B. (2022). Geospatial Big Data Platforms: A Comprehensive Review. KN - Journal of Cartography and Geographic Information, 72(4), 293–308. https://doi.org/10.1007/s42489-022-00121-7
Luo, L., Wang, X., Guo, H., Lasaponara, R., Zong, X., Masini, N., Wang, G., Shi, P., Khatteli, H., Chen, F., Tariq, S., Shao, J., Bachagha, N., Yang, R., & Yao, Y. (2019). Airborne and spaceborne remote sensing for archaeological and cultural heritage applications: A review of the century (1907–2017). Remote Sensing of Environment, 232(March), 111280. https://doi.org/10.1016/j.rse.2019.111280
Microsoft Open Source, McFarland, M., Emanuele, R., Morris, D., & Augspurger, T. (2022). microsoft/PlanetaryComputer: October 2022 (2022.10.28) [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.7261897
Orengo, H. A., Conesa, F. C., Garcia-Molsosa, A., Lobo, A., Green, A. S., Madella, M., & Petrie, C. A. (2020). Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data. Proceedings of the National Academy of Sciences, 117(31), 18240–18250. https://doi.org/10.1073/pnas.2005583117
Orengo, H., & Petrie, C. (2017). Large-Scale, Multi-Temporal Remote Sensing of Palaeo-River Networks: A Case Study from Northwest India and its Implications for the Indus Civilisation. Remote Sensing, 9(7), 735. https://doi.org/10.3390/rs9070735
Sentinel Hub. (2023). Sinergise Solutions d.o.o., a Planet Labs Company. https://www.sentinel-hub.com/
Visentin, D., Carrer, F., Fontana, F., Cavulli, F., Cesco Frare, P., Mondini, C., & Pedrotti, A. (2016). Prehistoric landscapes of the Dolomites: Survey data from the highland territory of Cadore (Belluno Dolomites, Northern Italy). Quaternary International, 402, 5–14. https://doi.org/10.1016/j.quaint.2015.10.080
Опубликован
2023-12-21
Как цитировать
Vasyunin, Yaroslav, Ceslav Cieslar, и Cristhian Kunkel. 2023. Satellite Big Data for Archaeological Discovery: A Digital Earth Perspective. Геоконтекст 11 (1), 24-33. https://geo-context.org/index.php/geocontext/article/view/70.
Раздел
Статьи
