Earth Observation Applications to Landslide Mapping, Monitoring and Modeling

Section 1: Introduction<br><br>1. A review of UAV-based data applications for landslide mapping and monitoring<br>2. A review of the state-of-the-art use of satellite Earth observation data for landslide mapping and monitoring<br><br>Section 2: Satellite data in landslide mapping and<br>monitoring<br><br>3. On the use of the EGMS data for studying landslides in Great Britain<br>4. Deciphering the kinematics of urban landslides through SAR imagery analysis<br>5. Artificial intelligence applications for landslide mapping/monitoring on satellite EO data<br>6. Mapping landslides on Earth, Moon, and Mars using satellite imagery and deep learning techniques<br><br>Section 3: Drone applications for landslide mapping<br>and monitoring<br><br>7. Landslide volume and runoff monitoring using UAV photogrammetry<br>8. Landslide 3D reconstruction and monitoring using oblique and nadiral drone aerial imagery<br>9. Geomorphic monitoring and assessment of debris flows using drone-based structure from motion<br>10. Machine learning and object-based image analysis for landside mapping using UAV-derived data<br>11. Estimating kinematic uncertainties of landslides using UAV time series<br>12. Detailed landslide kinematics mapping using short-term UAV time-series. Case study: Livadea landslide, Romania<br><br>Section 4: EO data assimilations in landslide susceptibility, hazard mapping and risk assessment<br><br>13. Building landslide inventory with LiDAR data and deep learning<br>14 Landslide susceptibility mapping using machine-learning algorithms and earth observation data<br>15. Microwave remote sensing for investigating hydrological preconditions triggering landslides: a case study: Ialomita Subcarpathians, Romania<br>16. Use of UAV imagery for the detection and measurement of damages to road networks in landslide areas<br><br>Section 5: Future challenges and future outlook<br><br>17. Mapping the existing challenges and pathway forward