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Filippo Catani

Filippo Catani

D-Index & Metrics

Earth Science

D-Index
63
Citations
12967
World Ranking
1615
National Ranking
17

Overview

Filippo Catani is affiliated with the University of Florence in Italy. Their research primarily focuses on environmental science and engineering, with significant contributions to the study of landslides and related hazards. The scholar's work spans multiple subfields, including management, monitoring, policy and law; atmospheric science; global and planetary change; safety, risk, reliability and quality; and mechanical engineering.

Catani's research addresses a range of topics such as landslides and related hazards, cryospheric studies and observations, flood risk assessment and management, geotechnical engineering and analysis, tree root and stability studies, fire effects on ecosystems, and synthetic aperture radar (SAR) applications and techniques.

They have a record of publishing in several notable venues, frequently contributing to:

  • Landslides
  • Remote Sensing
  • Journal of Rock Mechanics and Geotechnical Engineering
  • Engineering Geology
  • Scientific Reports

Among recent publications featuring Catani's work are the following papers:

  • "Landslide detection by deep learning of non-nadiral and crowdsourced optical images," 2020, Landslides
  • "Landslide detection in the Himalayas using machine learning algorithms and U-Net," 2022, Landslides
  • "Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors," 2022, Journal of Rock Mechanics and Geotechnical Engineering
  • "Landslide susceptibility assessment in complex geological settings: sensitivity to geological information and insights on its parameterization," 2020, Landslides
  • "Effect of antecedent rainfall conditions and their variations on shallow landslide-triggering rainfall thresholds in South Korea," 2020, Landslides

Catani collaborates frequently with peers in their field. Notable co-authors include:

  • Faming Huang
  • Jinsong Huang
  • Lorenzo Nava
  • Sansar Raj Meena
  • Kushanav Bhuyan

The researcher's interdisciplinary approach integrates engineering principles with environmental science, supporting the development of predictive models and risk assessment methods related to hillslope stability and landslide phenomena. Their work often involves the application of machine learning algorithms and remote sensing techniques.

Best Publications

  • Recommendations for the quantitative analysis of landslide risk

    J. Corominas;C.J. van Westen;P. Frattini;L. Cascini

  • Artificial Neural Networks applied to landslide susceptibility assessment

    Leonardo Ermini;Filippo Catani;Nicola Casagli

  • Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues

    F. Catani;D. Lagomarsino;S. Segoni;V. Tofani

  • Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China

    Chao Zhou;Chao Zhou;Kunlong Yin;Ying Cao;Bayes Ahmed

  • Landslide prediction, monitoring and early warning: a concise review of state-of-the-art

    Byung-Gon Chae;Hyuck-Jin Park;Filippo Catani;Alessandro Simoni

  • Monitoring, prediction, and early warning using ground-based radar interferometry

    Nicola Casagli;Filippo Catani;Chiara Del Ventisette;Guido Luzi

  • Statistical analysis of drainage density from digital terrain data

    Gregory E Tucker;Filippo Catani;Andrea Rinaldo;Rafael L Bras

  • Landslide hazard and risk mapping at catchment scale in the Arno River basin

    F. Catani;N. Casagli;L. Ermini;Gaia Righini

  • Rainfall thresholds for the forecasting of landslide occurrence at regional scale

    G. Martelloni;S. Segoni;R. Fanti;F. Catani

  • Persistent Scatterer Interferometry (PSI) Technique for Landslide Characterization and Monitoring

    Veronica Tofani;Federico Raspini;Filippo Catani;Nicola Casagli

  • The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: geomorphological features and landslide distribution

    A. Rosi;V. Tofani;L. Tanteri;C. Tacconi Stefanelli

  • Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors

    Unknown

  • An empirical geomorphology-based approach to the spatial prediction of soil thickness at catchment scale

    Filippo Catani;Samuele Segoni;Giacomo Falorni

  • Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method

    Chao Zhou;Chao Zhou;Kunlong Yin;Ying Cao;Emanuele Intrieri

  • Landslides triggered by rainfall: A semi-automated procedure to define consistent intensity-duration thresholds

    Samuele Segoni;Guglielmo Rossi;Ascanio Rosi;Filippo Catani

  • HIRESSS: a physically based slope stability simulator for HPC applications

    Guglielmo Rossi;Filippo Catani;Lorenzo Leoni;Samuele Segoni

  • Technical note: use of remote sensing for landslide studies in Europe

    Veronica Tofani;Samuele Segoni;Andrea Agostini;Filippo Catani

  • On the application of SAR interferometry to geomorphological studies: estimation of landform attributes and mass movements

    Filippo Catani;Paolo Farina;Sandro Moretti;Giovanni Nico

  • Geomorphic indexing of landslide dams evolution

    Carlo Tacconi Stefanelli;Samuele Segoni;Nicola Casagli;Filippo Catani

  • Landslide susceptibility map refinement using PSInSAR data

    Andrea Ciampalini;Federico Raspini;Daniela Lagomarsino;Filippo Catani

  • Persistent Scatterers Interferometry Hotspot and Cluster Analysis PSI-HCA for detection of extremely slow-moving landslides

    Ping Lu;Nicola Casagli;Filippo Catani;Veronica Tofani

  • Combination of Rainfall Thresholds and Susceptibility Maps for Dynamic Landslide Hazard Assessment at Regional Scale

    Samuele Segoni;Veronica Tofani;Ascanio Rosi;Filippo Catani

Frequent Co-Authors

Nicola Casagli
Nicola Casagli University of Florence
Samuele Segoni
Samuele Segoni University of Florence
Veronica Tofani
Veronica Tofani University of Florence
Sandro Moretti
Sandro Moretti University of Florence
Federico Raspini
Federico Raspini University of Florence
Silvia Bianchini
Silvia Bianchini University of Florence
Adam Emmer
Adam Emmer University of Graz
Paolo Frattini
Paolo Frattini University of Milano-Bicocca
Simonetta Paloscia
Simonetta Paloscia National Research Council (CNR)
Gabriele Moser
Gabriele Moser University of Genoa

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