AP23489830 " Development of integrated remote sensing and machine learning technologies for landslide monitoring and assessment"

Relevance

The use of multidimensional data and machine learning algorithms can improve forecasting accuracy and reduce the likelihood of errors in risk assessment. These methods help to manage danger zones more effectively and reduce potential damage from landslides.

Purpose

The main objective of the work is to develop and implement integrated technologies for remote sensing of the Earth (remote sensing) and machine learning for improved monitoring and assessment of landslide risks.

Expected and achieved results

In the first year, archival topographic, satellite, hydrogeological, and meteorological data were collected, a database structure was formed, and preliminary classification of deformation areas was carried out. Zones with a probability of landslide activity were identified. A baseline GNSS measurement series was conducted, and interferometric processing of the first series of images was performed.

Research team members with their identifiers (Scopus Author ID, Researcher ID, ORCID, if available) and links to relevant profiles