Patrick A Campbell
Biographic Data
| ID | 9720517 |
|---|---|
| NAME | Patrick A Campbell |
| GIVEN NAMES | Patrick A |
| FAMILY NAME | Campbell |
| SIGNATURE | CAMPBELL P A |
| AFFILIATIONS | NOAA Oceanic and Atmospheric Research |
| ORCID | 0000-0001-8571-9222 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
CNN-Based Building Type Classification into EF Scale Categories to Support Tornado Damage Assessments
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
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, …
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Building Classification Using Random Forest to Develop a Geodatabase for Probabilistic Hazard Information
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
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)