ASSESSING RESILIENCE OF INFRASTRUCTURES TOWARDS EXOGENOUS EVENTS BY USING PS-INSAR-BASED SURFACE MOTION ESTIMATES AND MACHINE LEARNING REGRESSION TECHNIQUES

7 citations as recorded by crossref.

1. Novel MLR-RF-Based Geospatial Techniques: A Comparison with OK W. Ahmed et al. 10.3390/ijgi11070371
2. Comparing the Performance of Historical and Regular Stone Pavement Structures in Urban Trafficked Areas through the Finite Element Method (FEM) N. Fiorentini et al. 10.3390/infrastructures8070115
3. Surface Motion Prediction and Mapping for Road Infrastructures Management by PS-InSAR Measurements and Machine Learning Algorithms N. Fiorentini et al. 10.3390/rs12233976
4. Can Machine Learning and PS-InSAR Reliably Stand in for Road Profilometric Surveys? N. Fiorentini et al. 10.3390/s21103377
5. Neural Network Pattern Recognition Experiments Toward a Fully Automatic Detection of Anomalies in InSAR Time Series of Surface Deformation P. Milillo et al. 10.3389/feart.2021.728643
6. Slope-Scale Evolution Categorization of Deep-Seated Slope Deformation Phenomena with Sentinel-1 Data D. Cardone et al. 10.3390/rs15235440
7. Coherence-Based Prediction of Multi-Temporal InSAR Measurement Availability for Infrastructure Monitoring D. Malinowska et al. 10.1109/JSTARS.2024.3449688