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Geospatial Big Data Platforms: A Comprehensive Review

Zusammenfassung": Geospatial Big Data Platforms: ein umfassender Überblick

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Abstract

Over the past decade, big data incorporating a spatial component “GEOSPATIAL BIG DATA” has become a global focus, increasingly attracting the attention of academia, industry, government and other organizations. The possibility of managing and processing geospatial big data to help decision-making therefore appears to be an important scientific and societal issue. But it is difficult to store, manage, process, analyze, visualize and extract useful information from geospatial big data using traditional approaches on local machines. In this article, a survey of geospatial big data platforms was conducted. In this context, several studies of the literature have been evaluated in terms of the different technologies and the main platforms for processing geospatial big data. This article is intended to guide researchers working on geospatial big data applications.

Zusammenfassung

Seit den letzten zehn Jahren erfährt die Forschung zu Big Data, mit einer räumlichen Komponente als „Geospatial Big Data“ einen globalen Fokus und zieht zunehmend die Aufmerksamkeit von Wissenschaft, Industrie, Regierungsorganisationen und weiteren Einrichtungen auf sich. Die Möglichkeit, Geospatial Big Data zu verwalten und zu verarbeiten, um die Entscheidungsfindung zu unterstützen, scheint daher ein wichtiges wissenschaftliches und gesellschaftliches Problem zu sein. Dennoch bleibt es weiterhin eine Herausforderung, nützliche Informationen aus Georäumliche Big Data mit traditionellen Ansätzen auf lokalen Maschinen zu speichern, zu verwalten, zu verarbeiten, zu analysieren, zu visualisieren und zu extrahieren.

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References

  • Alwahas A (2021) Using Google Earth Engine for the automated mapping of center pivot irrigation fields in Saudi Arabia. Thèse de doctorat

  • Amani M, Ghorbanian A, Ahmadi SA, Kakooei M, Moghimi A, Mirmazloumi SM, Moghaddam SHA, Mahdavi S, Ghahremanloo M, Parsian S, Wu Q, Brisco B (2020) Google earth engine cloud computing platform for remote sensing big data applications: a comprehensive review. IEEE J Selected Topics Appl Earth Observ Remote Sens 13:5326–5350. https://doi.org/10.1109/jstars.2020.3021052

    Article  Google Scholar 

  • Amitrano D, Guida R, Ruello G (2019) Multitemporal SAR RGB processing for Sentinel-1 GRD products: methodology and applications. IEEE J Selected Topics Appl Earth Observ Remote Sens 2(5):1497–1507

    Article  Google Scholar 

  • Dumoulin M (2014) Personalized large scale classification ofpublic tenders on hadoop

  • Eldawy A, Niu L, Haynes D et al (2017) Large scale analytics of vector+ raster big spatial data. In: Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. pp 1–4

  • Gomes V, Queiroz G, Ferreira K (2020) An overview of platforms for big earth observation data management and analysis. Remote Sensing 12:1253. https://doi.org/10.3390/rs12081253

    Article  Google Scholar 

  • Gong J, Peng Y, Zhou H (2010) Geoprocessing in the Microsoft cloud computing platform-azure. In: Proceedings the joint symposium of ISPRS technical commission IV & autocarto. Citeseer, p 6

  • Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031

    Article  Google Scholar 

  • Gumma MK, Thenkabail PS, Teluguntla PG, Oliphant A, Xiong J, Giri C, Pyla V, Dixit S, Whitbread AM (2020) Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud. Giscience & Remote Sens 57:302–322. https://doi.org/10.1080/15481603.2019.1690780

    Article  Google Scholar 

  • Hagedorn S, Götze P, Sattler K-U (2017) Big spatial data processing frameworks: feature and performance evaluation. In: EDBT, pp 490–493

  • Hansen M, Potapov P, Moore R, Hancher M, Turubanova S, Tyukavina D, Stehman S, Goetz S, Loveland T, Kommareddy A (2013) Observing the forest and the trees: the first high resolution global maps of forest cover change. Science 342:850–853

    Article  Google Scholar 

  • Haynes D, Ray S, Manson SM et al (2015) High performance analysis of big spatial data. In: 2015 IEEE International Conference on Big Data (Big Data). IEEE, pp 1953–1957

  • Kaur K, Sehra SS, Arora P (2017) A review on spatial data and spatial hadoop. Int J Latest Trends Eng Tech 8(1):545–550

    Google Scholar 

  • Khan R, Gilani H (2021) Global drought monitoring with big geospatial datasets using Google Earth Engine. Environ Sci Pollut Res 28:17244–17264. https://doi.org/10.1007/s11356-020-12023-0

    Article  Google Scholar 

  • Khan R, Gilani H, Iqbal N, Shahid I (2020) Satellite-based (2000–2015) drought hazard assessment with indices, mapping, and monitoring of Potohar plateau, Punjab, Pakistan. Environ Earth Sci. https://doi.org/10.1007/s12665-019-8751-9

    Article  Google Scholar 

  • Kakooei M, Baleghi Y (2020) A two-level fusion for building irregularity detection in post-disaster VHR oblique images. Earth Sci Info 13(2):459–477

    Article  Google Scholar 

  • Kumar L, Mutanga O (2018) Google Earth Engine applications since inception: usage, trends, and potential. Remote Sensing 10:1509. https://doi.org/10.3390/rs10101509

    Article  Google Scholar 

  • Laney D et al (2001) 3D data management: controlling data volume, velocity and variety. META group research note, vol. 6, no 70, p 1

  • Li Z (2020) Geospatial big data handling with high performance computing: current approaches and future directions. Geotech Environ. https://doi.org/10.1007/978-3-030-47998-5_4

    Article  Google Scholar 

  • Li S, Dragicevic S, Castro FA et al (2016) Geospatial big data handling theory and methods: a review and research challenges. ISPRS J Photogram Remote Sens 115:119–133

    Article  Google Scholar 

  • Liu X, Hao L, Yang W (2019) BiGeo: a foundational PaaS framework for efficient storage, visualization, management, analysis, service, and migration of geospatial big data—a case study of Sichuan Province, China. ISPRS Int J Geo-Info 8:449. https://doi.org/10.3390/ijgi8100449

    Article  Google Scholar 

  • Lenka RK, Barik RK, Gupta N et al (2016) Comparative analysis of SpatialHadoop and GeoSpark for geospatial big data analytics. In: 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I). IEEE, pp 484–488

  • Longetal T (2019) 30m resolution global annual burned area mapping based on Landsat Images and Google Earth Engine. Remote Sens 11(5):489

    Article  Google Scholar 

  • Manyika J (2011) Big data: The next frontier for innovation, competition, and productivity. http://www.mckinsey.com/Insights/MGI/Research/Technology_and_Innovation/Big_data_The_next_frontier_for_innovation

  • Meilianda E, Pradhan B, Comfort LK et al (2019) Assessment of post-tsunami disaster land use/land cover change and potential impact of future sea-level rise to low-lying coastal areas: a case study of Banda Aceh coast of Indonesia. Int J Disaster Risk Reduction 41:101292

    Article  Google Scholar 

  • Morais CD (2012) Where is the Phrase « 80% of Data is Geographic » From? Retrieved May 2, 2015, from https://www.gislounge.com/80-percent-data-is-geographic/

  • Praveen P, Jayanth Babu CH, Rama B (2016) Big data environment for geospatial data analysis. In: 2016 International Conference on Communication and Electronics Systems (ICCES). IEEE, pp 1–6

  • Sarricolea P et al (2020) Recent wildfires in central Chile: Detecting links between burned areas and population exposure in the wildland urban interface. Sci Total Environ 706:135894

    Article  Google Scholar 

  • Reinsel D, Gantz J, Rydning J (2018) Data age 2025: the digitization of the world from edge to core. Seagate, https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf

  • Roberts LJ, Burnett R, Tietz J, Veloz S (2019) Recent drought and tree mortality effects on the avian community in southern Sierra Nevada: a glimpse of the future? Ecol Appl 29:e01848. https://doi.org/10.1002/eap.1848

    Article  Google Scholar 

  • Sedona Apache. Available online: https://sedona.apache.org/. Accessed 30 Jan 2022

  • Sentinel hub-esa. Available online: https://eo4society.esa.int/2019/04/26/sentinel-hub-crosses-100m-requests-per-month/. Accessed 30 Mar 2022

  • Sinergise. Sentinel Hub by Sinergise. 2020. Available online: https://www.sentinel-hub.com/. Accessed 30 Mar 2022

  • Shrivastava S (2020) A review of spatial big data platforms, opportunities, and challenges. IETE J Educ 61(2):80–89

    Article  Google Scholar 

  • Singla S (2021) Raptor: large scale processing of big raster+ vector data. In: Proceedings of the 2021 International Conference on Management of Data, pp 2905–2907

  • Singla S, Eldawy A (2020) Raptor zonal statistics: fully distributed zonal statistics of big raster+ vector data. In: 2020 IEEE International Conference on Big Data (Big Data), IEEE, pp 571–580

  • Singla S, Eldawy A, Diao T et al (2021) Experimental study of big raster and vector database systems. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, pp 2243–2248

  • Stonebraker M (2010) SQL databases v. NoSQL databases. Commun ACM 53(4):10–11

    Article  Google Scholar 

  • Tamiminia H, Salehi B, Mahdianpari M, Quackenbush L, Adeli S, Brisco B (2020) Google Earth Engine for geo-big data applications: a meta-analysis and systematic review. ISPRS J Photogram Remote Sens 164:152–170. https://doi.org/10.1016/j.isprsjprs.2020.04.001

    Article  Google Scholar 

  • Tiwari V, Kumar V, Matin MA, Thapa A, Ellenburg WL, Gupta N, Thapa S (2020) Flood inundation mapping- Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine. PLoS ONE 15:e0237324. https://doi.org/10.1371/journal.pone.0237324

    Article  Google Scholar 

  • Uddin MM (2019) Operational flood mapping using multi-temporal sentinel-1 SAR Images: a case study from Bangladesh. Remote Sensing 11:1581. https://doi.org/10.3390/rs11131581

    Article  Google Scholar 

  • White T (2012) Hadoop: The definitive guide. O’Reilly Media Inc

    Google Scholar 

  • Yang C, Huang Q, Li Z, Xu C, Liu K (2014) Spatial cloud computing: a practical approach. CRC Press, Boca Raton, FL

    Google Scholar 

  • Yu B, Chen F, Muhammad S (2018) Analysis of satellite-derived landslide at Central Nepal from 2011 to 2016. Environ Earth Sci. https://doi.org/10.1007/s12665-018-7516-1

    Article  Google Scholar 

  • Yu J, Wu J, Sarwat M (2015) Geospark: a cluster computing framework for processing large-scale spatial data. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp 1–4

  • Yu S-C, Shin D-B, Ahn J-W (2016) A study on concepts and utilization of Geo-Spatial Big Data in South Korea. KSCE J Civil Eng 20(7):2893–2901

    Article  Google Scholar 

  • Yu J, Zhang Z, Sarwat M (2019) Spatial data management in apache spark: The geospark perspective and beyond. GeoInformatica 23(1):37–78

    Article  Google Scholar 

  • Zaharia M, Chowdhury M, Franklin MJ et al (2010) Spark: Cluster computing with working sets. HotCloud 10(10–10):95

    Google Scholar 

Download references

Acknowledgements

This research was made possible by Intelligent Systems, Georesources and Renewable Energies Laboratory of Sidi Mohamed Ben Abdellah University of Fez. The authors thank Professor Younes Lakhrissi for assisting the lead author in the realization of this work.

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Correspondence to Yassine Loukili.

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Loukili, Y., Lakhrissi, Y. & Ali, S.E.B. Geospatial Big Data Platforms: A Comprehensive Review. KN J. Cartogr. Geogr. Inf. 72, 293–308 (2022). https://doi.org/10.1007/s42489-022-00121-7

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