Spatial Feature Fusion

Spatial Feature Fusion is a geospatial data integration methodology that combines multiple spatially referenced feature representations through rule-based or learned transformation rules to generate a unified, semantically enhanced feature space while preserving topological fidelity across disparate geographic scales and modalities.

237

Publications

10.4K

Citations

934

Authors

292

Institutions

Publications per year

2017–2026

216

Authors

934

  1. HW
    Haoqian Wang

    Tsinghua University, China

    2 publications · 209 citations

    2

    h-index

  2. KU
    Kurban Ubul

    Xinjiang University, China

    1 publications · 12 citations

    2

    h-index

  3. XY
    Xin Yuan

    Westlake University, China

    2 publications · 209 citations

    2

    h-index

  4. QL
    Qingquan Li

    Shenzhen University, China

    2 publications · 220 citations

    2

    h-index

  5. LW
    Lizhe Wang

    China University of Geosciences, China

    2 publications · 48 citations

    2

    h-index

1–5 of 934

Venues

  1. Remote Sensing

    15 publications · 564 citations

    14

    h-index

  2. IEEE Access

    11 publications · 365 citations

    11

    h-index

  3. 10

    h-index

  4. Sensors

    8 publications · 228 citations

    9

    h-index

  5. 6

    h-index

1–5