Umut Lagap
Biographic Data
| ID | 9962133 |
|---|---|
| NAME | Umut Lagap |
| GIVEN NAMES | Umut |
| FAMILY NAME | Lagap |
| SIGNATURE | LAGAP U |
| AFFILIATIONS | National Science and Technology Center for Disaster Reduction |
| ORCID | 0000-0003-4498-1501 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Towards reliable deep learning for post-disaster damage Assessment
The increasing frequency and severity of natural hazard-induced disasters necessitate rapid and reliable post-disaster damage detection (PDD) to inform disaster response and recovery. Deep learning (DL) models, when paired with remote sensing (RS) data, have shown potential in this domain, but challenges persist due to limited interpretability and inconsistent reliability, particularly for high-severity damage classes. This study investigates the…
Digital post-disaster risk management twinning
Digital Twins (DT) is the real-time virtual representation of systems, communities, cities, or even human beings with the substantial potential to revolutionize post-disaster risk management efforts and achieve resilient communities against the adverse effects of disasters. However, this potential remains largely unrecognized and poorly understood in disaster risk management. This study explores current achievements, existing challenges, and the …
No prominent works on this page.
Digital post-disaster risk management twinning
Digital Twins (DT) is the real-time virtual representation of systems, communities, cities, or even human beings with the substantial potential to revolutionize post-disaster risk management efforts and achieve resilient communities against the adverse effects of disasters. However, this potential remains largely unrecognized and poorly understood in disaster risk management. This study explores current achievements, existing challenges, and the …
Towards reliable deep learning for post-disaster damage Assessment
The increasing frequency and severity of natural hazard-induced disasters necessitate rapid and reliable post-disaster damage detection (PDD) to inform disaster response and recovery. Deep learning (DL) models, when paired with remote sensing (RS) data, have shown potential in this domain, but challenges persist due to limited interpretability and inconsistent reliability, particularly for high-severity damage classes. This study investigates the…
Anomaly Detection Techniques and Applications (1 work) · Business (1 work) · Computer Science (1 work) · Conceptual framework (1 work) · COVID-19 diagnosis using AI (1 work) · Data science (1 work) · Deep learning (1 work) · Digital Transformation in Industry (1 work) · Emergency management (1 work) · Engineering (1 work)