Populating a catalogue with displacement vs. time data: a tool for typifing landslides kinematic and a support for sustainable risk management

Collection of data from landslides monitoring is crucial for a sustainable risk management. With this aim, the integrated monitoring systems combining in situ and remote sensing techniques provide a comprehensive understanding of landslide activity. One of the tasks of the Innovation Ecosystem "Tech4You - Technologies for Climate Change Adaptation and Quality of Life Improvement" focuses on analysing case studies to compare different landslide types, their associated monitoring networks and the displacements entity.

A key objective is to create a catalogue of displacements for typifying landslides. To achieve this goal, a comprehensive literature review was conducted. Only landslides with displacement data over time were considered. The catalogue records the landslide type, location, monitoring system, sensor type, installation year, monitoring period, and main dimensions.

A notable challenge in this research was the limited availability of raw displacement data. Many studies present monitoring results in graphical form, often as images, making numerical data extraction difficult. To overcome this, software tools and artificial intelligence (AI) methods have been employed to analyse graph images and extract numerical values. However, AI often encounters limitations in accurately interpreting and extracting numerical values from diverse graph formats. While AI offers rapid initial analyses, the use of dedicated software guarantees precision in data extraction. The combined workflow of inspection, validation, and software application ensures reliable outcomes, making the process more efficient than manual or traditional methods.

The catalogue now includes more than 60 classified landslides, and research on new case studies is always ongoing. For this reason, and to overcome the limitation of the reduce number of studies with associated data, this work serves as encouragement to increase the number of cases registered in the database.

A specialized digital tool will be developed to integrate in a general platform and utilize collected landslide displacement data. This platform aims to: i) support local and national public institutions, ii) facilitate widespread access to and utilization of the data for monitoring and mitigating landslide risk, and iii) assist in the identification and classification of landslides with characteristics similar to those catalogued in the database.

ACKNOWLEDGEMENTS

This work was funded by the Next Generation EU - Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of Innovation Ecosystems', building 'Territorial R&D Leaders' (Directorial Decree n. 2021/3277) - project Tech4You – Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.