Jooho Kim
Biographic Data
| ID | 9720514 |
|---|---|
| NAME | Jooho Kim |
| GIVEN NAMES | Jooho |
| FAMILY NAME | Kim |
| SIGNATURE | KIM J |
| AFFILIATIONS | Texas A&M University |
| ORCID | 0000-0002-0395-5107 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 7 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Enhancing community-based participatory flood imagery using an AI-based super-resolution framework
Imagery and videos contributed by local communities provide valuable ground-level perspectives of disaster conditions, particularly in suburban and rural areas where monitoring infrastructure is sparse. However, community-based participatory visual data are often degraded by low resolution, motion blur, compression artifacts, and inconsistent metadata. These limitations are further compounded when imagery and videos are captured using older or lo…
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…
GeoSight
Emergency water distribution systems to improve spatial equality and spatial equity in a heterogeneous community with differing mobility characteristics
An Agent-Based Modeling Approach to Protective Action Decision-Related Travel during Tornado Warnings
Tornadoes represent a significant threat to life and property and tend to evoke protective action in most people. Studies have suggested that many people travel to the nearest storm shelter or flee the area, rather than sheltering-in-place as recommended by the National Weather Service (NWS). While shelter-in-place is the recommendation of the NWS, for tornado safety, few studies have quantified the risk reduction when compared to traveling to a …
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, …
Social network analysis
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Social network analysis
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, …
Emergency water distribution systems to improve spatial equality and spatial equity in a heterogeneous community with differing mobility characteristics
An Agent-Based Modeling Approach to Protective Action Decision-Related Travel during Tornado Warnings
Tornadoes represent a significant threat to life and property and tend to evoke protective action in most people. Studies have suggested that many people travel to the nearest storm shelter or flee the area, rather than sheltering-in-place as recommended by the National Weather Service (NWS). While shelter-in-place is the recommendation of the NWS, for tornado safety, few studies have quantified the risk reduction when compared to traveling to a …
GeoSight
Enhancing community-based participatory flood imagery using an AI-based super-resolution framework
Imagery and videos contributed by local communities provide valuable ground-level perspectives of disaster conditions, particularly in suburban and rural areas where monitoring infrastructure is sparse. However, community-based participatory visual data are often degraded by low resolution, motion blur, compression artifacts, and inconsistent metadata. These limitations are further compounded when imagery and videos are captured using older or lo…
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…
Computer Science (5 works) · Engineering (4 works) · Environmental Science (3 works) · Flood Risk Assessment and Management (3 works) · Geography (3 works) · Computer security (2 works) · Emergency management (2 works) · Evacuation and Crowd Dynamics (2 works) · Flood myth (2 works) · Hazard (2 works)