Kristin M Calhoun
Biographic Data
| ID | 9720518 |
|---|---|
| NAME | Kristin M Calhoun |
| GIVEN NAMES | Kristin M |
| FAMILY NAME | Calhoun |
| SIGNATURE | CALHOUN K M |
| AFFILIATIONS | NOAA Oceanic and Atmospheric Research |
| ORCID | 0000-0003-2858-256X |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| 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…
Lightning Safety and Hazard Risk Assessment for Division One College Football
Lightning often remains an underrecognized natural hazard to the general public. National Collegiate Athletic Association (NCAA) Division One college football games attract thousands of people, yet lightning safety regulations remain underdeveloped. To better understand the vulnerability and actions of college football spectators, we developed and distributed a survey to attendees on college game days at the University of Oklahoma. The survey add…
No prominent works on this page.
Lightning Safety and Hazard Risk Assessment for Division One College Football
Lightning often remains an underrecognized natural hazard to the general public. National Collegiate Athletic Association (NCAA) Division One college football games attract thousands of people, yet lightning safety regulations remain underdeveloped. To better understand the vulnerability and actions of college football spectators, we developed and distributed a survey to attendees on college game days at the University of Oklahoma. The survey add…
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) · Convolutional neural network (1 works) · Data set (1 works) · Engineering (1 works) · Evacuation and Crowd Dynamics (1 works) · Fire effects on ecosystems (1 works) · Football (1 works) · Forensic engineering (1 works) · Fragility (1 works) · Geography (1 works)