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

Lorenzo Palombi; Gabriele Amato; Valentina Raimondi

doi:10.1117/12.2638388

26 October 2022 National scale classification of landslide types by a data–driven approach and artificial neural networks

Lorenzo Palombi, Gabriele Amato, Valentina Raimondi

Author Affiliations +

Proceedings Volume 12268, Earth Resources and Environmental Remote Sensing/GIS Applications XIII; 122680P (2022) https://doi.org/10.1117/12.2638388
Event: SPIE Remote Sensing, 2022, Berlin, Germany

Abstract

Classification of landslide type is important in risk management, yet it is often missing in large inventories. Here we present a novel data-driven method that uses morphometric and geospatial input parameters to classify landslides type at a national scale in Italy by means of a shallow Artificial Neural Network. The overall True Positive Rate is 0.76 for a five-class classification of over 275000 landslides. The performances in the entire national territory are very good, with F-score higher than 0.9 in large areas. The method can be applied to any polygonal inventory, as those produced by automatic mapping from Earth Observation imagery.

Conference Presentation

Citation Download Citation

Lorenzo Palombi, Gabriele Amato, and Valentina Raimondi "National scale classification of landslide types by a data–driven approach and artificial neural networks", Proc. SPIE 12268, Earth Resources and Environmental Remote Sensing/GIS Applications XIII, 122680P (26 October 2022); https://doi.org/10.1117/12.2638388

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