Gabriele Amato

Accounting for covariate distributions in slope-unit-based landslide susceptibility models. A case study in the alpine environment

L. Lombardo, G. Amato, C. Eisank, 2019, Engineering Geology.

New pedotransfer approaches to predict soil bulk density using WoSIS soil data and environmental covariates in Mediterranean agro-ecosystems.

L. Lombardo, M. Märker, C. Schillaci, 2021, The Science of the total environment.

Step-like displacements of a deep seated gravitational slope deformation observed during the 2016–2017 seismic events in Central Italy

Marco Polcari, Christian Bignami, Marco Moro, 2018, Engineering Geology.

Earthquake-triggered landslide susceptibility in Italy by means of Artificial Neural Network

L. Lombardo, G. Amato, S. Martino, 2021, Bulletin of Engineering Geology and the Environment.

Experimental Tests on TIR Multispectral Images for Temperature-Emissivity Separation by Using the MaxEnTES Algorithm

D. Guzzi, G. Amato, C. Lastri, 2019, Proceedings.

National scale classification of landslide types by a data–driven approach and artificial neural networks

G. Amato, V. Raimondi, L. Palombi, 2022, Remote Sensing.

Data-driven classification of landslide types at a national scale by using Artificial Neural Networks

Lorenzo Palombi, Valentina Raimondi, Gabriele Amato, 2021, Int. J. Appl. Earth Obs. Geoinformation.

Presence-only approach to assess landslide triggering-thickness susceptibility: a test for the Mili catchment (north-eastern Sicily, Italy)

G. Fubelli, L. Lombardo, G. Amato, 2015, Natural Hazards.