Veronica Tofani
Biographic Data
| ID | 8536673 |
|---|---|
| NAME | Veronica Tofani |
| GIVEN NAMES | Veronica |
| FAMILY NAME | Tofani |
| SIGNATURE | TOFANI V |
| AFFILIATIONS | University of Florence |
| ORCID | 0000-0003-2622-2034 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Mas-Land
Landslides require rapid, data-driven post-event assessment to support emergency response, yet most existing approaches focus on stand-alone landslide detection or mapping accuracy, with limited integration into operational decision-support workflows. This study presents MAS-LAND (Multi-Agent System for Landslide Detection and Rapid Response), a multi-agent, LLM-enhanced framework structured around three main stages: (1) post-event landslide dete…
Landslide susceptibility of the Prato–Pistoia–Lucca provinces, Tuscany, Italy
We mapped landslide susceptibility in the provinces of Lucca, Pistoia and Prato (central Italy), a 3103 km2 territory that approximately corresponds to the portion of Tuscany principally affected by landslides. We used a methodology based on a treebagger random forest. The input parameters used for the susceptibility assessment are curvature, flow accumulation, topographic wetness index, elevation, profile curvature, planar curvature, slope gradi…
No prominent works on this page.
Landslide susceptibility of the Prato–Pistoia–Lucca provinces, Tuscany, Italy
We mapped landslide susceptibility in the provinces of Lucca, Pistoia and Prato (central Italy), a 3103 km2 territory that approximately corresponds to the portion of Tuscany principally affected by landslides. We used a methodology based on a treebagger random forest. The input parameters used for the susceptibility assessment are curvature, flow accumulation, topographic wetness index, elevation, profile curvature, planar curvature, slope gradi…
Mas-Land
Landslides require rapid, data-driven post-event assessment to support emergency response, yet most existing approaches focus on stand-alone landslide detection or mapping accuracy, with limited integration into operational decision-support workflows. This study presents MAS-LAND (Multi-Agent System for Landslide Detection and Rapid Response), a multi-agent, LLM-enhanced framework structured around three main stages: (1) post-event landslide dete…
Landslide (2 works) · Landslides and related hazards (2 works) · Cartography (1 work) · Deformation monitoring (1 work) · Disaster Management and Resilience (1 work) · Fire effects on ecosystems (1 work) · Geography (1 work) · Geology (1 work) · Geomorphology (1 work) · Lithology (1 work)