Deep Learning Estimation

Deep Learning Estimation is a computational methodology within remote sensing that employs deep neural networks to automatically learn hierarchical features from high-dimensional data for the purpose of accurately predicting leaf area index, distinguishing itself from traditional empirical or shallow machine learning approaches through its capacity for end-to-end feature extraction and nonlinear modeling.

390

Publications

17.9K

Citations

1.8K

Authors

700

Institutions

Publications per year

2017–2026

379

Authors

1.8K

  1. JM
    Juncheng Ma

    Institute of Environment and Sustainable Development in Agriculture, China

    4 publications · 259 citations

    4

    h-index

  2. XL
    Xiaofeng Li

    Chinese Academy of Sciences, China

    5 publications · 222 citations

    4

    h-index

  3. AH
    Alf Harbitz

    Norwegian Institute of Marine Research, Norway

    3 publications · 84 citations

    3

    h-index

  4. SL
    Sophie Langer

    Technische Universität Darmstadt, Germany

    3 publications · 161 citations

    3

    h-index

  5. LZ
    Lingxian Zhang

    China Agricultural University, China

    3 publications · 236 citations

    3

    h-index

1–5 of 1.8K

Venues

  1. IEEE Access

    17 publications · 1K citations

    15

    h-index

  2. Sensors

    15 publications · 1.1K citations

    14

    h-index

  3. Remote Sensing

    15 publications · 907 citations

    14

    h-index

  4. arXiv (Cornell University)

    Repository · 11 publications · 358 citations

    11

    h-index

  5. Computers and Electronics in Agriculture

    9 publications · 545 citations

    9

    h-index

1–5