Multi-source Data Fusion

Multi-Source Data Fusion is a computational methodology that integrates heterogeneous data from multiple independent sources to generate a more accurate, consistent, and comprehensive representation of structural damage in post-earthquake assessment through the synergistic combination of complementary information.

982

Publications

34.2K

Citations

4.4K

Authors

1.1K

Institutions

Publications per year

2017–2026

850

Authors

4.4K

  1. JG
    Jing Gao

    University at Buffalo, State University of New York, United States

    5 publications · 637 citations

    5

    h-index

  2. TL
    Tianrui Li

    Southwest Jiaotong University, China

    5 publications · 281 citations

    5

    h-index

  3. LZ
    Limao Zhang

    Huazhong University of Science and Technology, China

    4 publications · 295 citations

    4

    h-index

  4. WX
    Weihua Xu

    Southwest University, China

    4 publications · 243 citations

    4

    h-index

  5. ZY
    Zhu‐Hong You

    Xinjiang Technical Institute of Physics & Chemistry, China

    4 publications · 148 citations

    4

    h-index

1–5 of 4.4K

Venues

  1. Remote Sensing

    58 publications · 1.7K citations

    25

    h-index

  2. Information Fusion

    18 publications · 976 citations

    15

    h-index

  3. IEEE Access

    20 publications · 719 citations

    15

    h-index

  4. Sensors

    16 publications · 308 citations

    13

    h-index

  5. 12

    h-index

1–5