Geospatial Big Data Processing

Geospatial Big Data Processing is a computational framework that integrates scalable algorithms, distributed systems, and spatial analytics to manage, process, and derive meaningful patterns from large-volume, high-velocity, and heterogeneous geospatial datasets, distinguishing it from conventional geospatial processing through its emphasis on volume, velocity, variety, and veracity in spatial data contexts.

332

Publications

30.4K

Citations

1.1K

Authors

443

Institutions

Publications per year

2017–2026

226

Authors

1.1K

  1. AE
    Ahmed Eldawy

    University of Minnesota, United States

    11 publications · 275 citations

    11

    h-index

  2. MF
    Mohamed F. Mokbel

    University of Minnesota, United States

    10 publications · 257 citations

    10

    h-index

  3. CY
    Chaowei Yang

    George Mason University, United States

    10 publications · 1.3K citations

    10

    h-index

  4. RK
    Rabindra K. Barik

    KIIT University, India

    7 publications · 241 citations

    7

    h-index

  5. LA
    Louai Alarabi

    Umm al-Qura University, Saudi Arabia

    6 publications · 122 citations

    6

    h-index

1–5 of 1.1K

Venues

  1. ISPRS International Journal of Geo-Information

    IJGI · 21 publications · 1.2K citations

    17

    h-index

  2. 12

    h-index

  3. Remote Sensing

    11 publications · 736 citations

    11

    h-index

  4. 8

    h-index

  5. Computers Environment and Urban Systems

    6 publications · 679 citations

    6

    h-index

1–5