Urban Infrastructure at Risk: Assessing Landslide Impacts

Landslides are hazardous phenomena that pose considerable threats to the environment and urban infrastructure. This study focuses on both detecting landslides from remote sensing imagery and evaluating the impact of landslides on urban infrastructure. For landslide detection, we implement a machine learning technique, i.e., Support Vector Machines (SVM) and deep-learning U-Net, in the Pomarico region of Italy. Sentinel-2 satellite data from 2019 were utilized for the analysis, with ground control points collected from the study area to validate the techniques' results. The findings demonstrated that the SVM and U-Net achieved overall accuracies of 87.00% and 91.00% in detecting landslides, respectively. Additionally, the study revealed that landslides displaced electrical poles across the region, caused significant damage to several buildings, and disrupted the existing road network. In summary, these results provide valuable insights for urban planners and administrators, helping to enhance the effectiveness of methods used to simulate landslides and assess their impact on urban infrastructure.