Ruthvik Kanumuri
Biographic Data
| ID | 9720515 |
|---|---|
| NAME | Ruthvik Kanumuri |
| GIVEN NAMES | Ruthvik |
| FAMILY NAME | Kanumuri |
| SIGNATURE | KANUMURI R |
| AFFILIATIONS | Texas A&M University |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2026 |
| 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…
No prominent works on this page.
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…
Advanced Image Processing Techniques (1 works) · Citizen journalism (1 works) · Convolutional neural network (1 works) · Data set (1 works) · Flood myth (1 works) · Flood Risk Assessment and Management (1 works) · Fragility (1 works) · Geospatial analysis (1 works) · Hazard (1 works) · Hazard analysis (1 works)