Spatial Anomaly Detection

Spatial Anomaly Detection is a type of anomaly detection method that identifies rare, atypical patterns or instances in spatial data by explicitly leveraging the geographic location, spatial relationships, and regional dependencies inherent in the data to distinguish anomalies from normal spatial occurrences.

223

Publications

13.1K

Citations

690

Authors

283

Institutions

Publications per year

2017–2026

105

Authors

690

  1. CL
    Chang‐Tien Lu

    Virginia Tech, United States

    14 publications · 870 citations

    13

    h-index

  2. VP
    Vandana P. Janeja

    University of Maryland, Baltimore County, United States

    7 publications · 197 citations

    7

    h-index

  3. YK
    Yufeng Kou

    Shanghai Jiao Tong University, China

    7 publications · 390 citations

    7

    h-index

  4. MD
    Min Deng

    Central South University, China

    5 publications · 104 citations

    5

    h-index

  5. DC
    Dechang Chen

    Uniformed Services University of the Health Sciences, United States

    5 publications · 465 citations

    5

    h-index

1–5 of 690

Venues

  1. 8

    h-index

  2. IEEE Access

    6 publications · 249 citations

    6

    h-index

  3. Data Mining and Knowledge Discovery

    5 publications · 416 citations

    5

    h-index

  4. Computers Environment and Urban Systems

    4 publications · 91 citations

    4

    h-index

  5. Intelligent Data Analysis

    IDA · 4 publications · 126 citations

    4

    h-index

1–5