Automated Feature Recognition

Automated Feature Recognition is a methodological framework within remote sensing and three-dimensional modeling automation that enables the systematic identification and extraction of geospatial features from sensor data through algorithmic processing, distinguishing itself from manual or semi-automated approaches by its reliance on computational intelligence to minimize human intervention.

203

Publications

10.4K

Citations

691

Authors

290

Institutions

Publications per year

2017–2026

63

Authors

691

  1. JJ
    Jami J. Shah

    Arizona State University, United States

    5 publications · 95 citations

    5

    h-index

  2. JH
    JungHyun Han

    Korea University, South Korea

    5 publications · 403 citations

    5

    h-index

  3. WC
    William C. Regli

    Drexel University, United States

    5 publications · 374 citations

    5

    h-index

  4. TJ
    Timothy J. Tautges

    Argonne National Laboratory, United States

    3 publications · 107 citations

    3

    h-index

  5. HS
    Hiroshi Sakurai

    The University of Osaka, Japan

    3 publications · 261 citations

    3

    h-index

1–5 of 691

Venues

  1. Computer-Aided Design

    10 publications · 547 citations

    10

    h-index

  2. International Journal of Production Research

    8 publications · 214 citations

    8

    h-index

  3. 6

    h-index

  4. 4

    h-index

  5. 3

    h-index

1–5