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Patrick A Campbell

Biographic Data

ID9720517
NAMEPatrick A Campbell
GIVEN NAMESPatrick A
FAMILY NAMECampbell
SIGNATURECAMPBELL P A
AFFILIATIONSNOAA Oceanic and Atmospheric Research
ORCID0000-0001-8571-9222
VERIFIEDYes
TOTAL WORKS2
TOTAL CITATIONS0
AUTHOR COUNT2
EDITOR COUNT0
FIRST PUBLICATION YEAR2022
LATEST PUBLICATION YEAR2026
H-INDEX0
  • CNN-Based Building Type Classification into EF Scale Categories to Support Tornado Damage Assessments

    Jooho Kim, Ruthvik Kanumuri et al.•ARTICLE•Natural Hazards Review•2026

    Accurate assessment of tornado impacts requires detailed information on building characteristics that influence vulnerability. This study investigates a convolutional neural network (CNN)-based approach to classify nonresidential buildings into enhanced Fujita (EF) damage categories using image data. Three deep learning architectures—CNN, ConvNeXt, and ResNet50—were evaluated on a data set representing 11 EF building categories. ResNet50 achieved…

  • Building Classification Using Random Forest to Develop a Geodatabase for Probabilistic Hazard Information

    Jooho Kim, Joshua J Hatzis et al.•ARTICLE•Natural Hazards Review•2022

    To understand the community risk from severe weather threats, two components, including weather information and community assets, are crucial. Recently, probabilistic hazard information (PHI) from the National Oceanic and Atmospheric Administration (NOAA) Forecasting a Continuum of Environmental Threats (FACETs) program has been developed to provide dynamic weather-related information between the watch and warning systems to weather forecasters, …

No prominent works on this page.

  • Building Classification Using Random Forest to Develop a Geodatabase for Probabilistic Hazard Information

    Jooho Kim, Joshua J Hatzis et al.•ARTICLE•Natural Hazards Review•2022

    To understand the community risk from severe weather threats, two components, including weather information and community assets, are crucial. Recently, probabilistic hazard information (PHI) from the National Oceanic and Atmospheric Administration (NOAA) Forecasting a Continuum of Environmental Threats (FACETs) program has been developed to provide dynamic weather-related information between the watch and warning systems to weather forecasters, …

  • CNN-Based Building Type Classification into EF Scale Categories to Support Tornado Damage Assessments

    Jooho Kim, Ruthvik Kanumuri et al.•ARTICLE•Natural Hazards Review•2026

    Accurate assessment of tornado impacts requires detailed information on building characteristics that influence vulnerability. This study investigates a convolutional neural network (CNN)-based approach to classify nonresidential buildings into enhanced Fujita (EF) damage categories using image data. Three deep learning architectures—CNN, ConvNeXt, and ResNet50—were evaluated on a data set representing 11 EF building categories. ResNet50 achieved…

Hazard (2 works) · Built environment (1 works) · Civil engineering (1 works) · Computer Science (1 works) · Convolutional neural network (1 works) · Data set (1 works) · Data-Driven Disease Surveillance (1 works) · Emergency management (1 works) · Engineering (1 works) · Environmental resource management (1 works)

Ethnos_APP • Open Source Project • MIT License • Frontend v2.0.0 • Privacy and Cookies • API Documentation: api.ethnos.app/docs • API Source Code: GitHub • DOI: 10.5281/zenodo.17049435 • Frontend Source Code: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae