Earth Observation Applications to Landslide Mapping, Monitoring and Modelling Cutting-Edge Approaches with Artificial Intelligence, Aerial and Satellite Imagery Earth Observation Series

Earth Observation Applications to Landslide Mapping, Monitoring and Modelling: Cutting-edge Approaches with Artificial Intelligence, Aerial and Satellite Imagery focuses on the applications of drone and satellite imagery for mapping landslides. Topics covered include the use of ultra-high aerial imagery, obtained with drones, for mapping and predicting landslide activity (like runoff, volume displacement, exposure to risk, etc.). Subjects related to EO data assimilations into landslide hazard prediction models are also included. The primary aim of this book is to advance the scientific understanding and application of technologies to address a variety of fields related to landslide mapping and monitoring.Because of the intensification of the catastrophic events in the last 20 years, landslide monitoring gained considerable interest from the geoscience community. This book will be beneficial for the postgraduates, PhD research scholars, professors and scientists, geospatial experts, modelers, oceanography scientists and conservation communities, environmental consultants, big data compilers, and computing experts.

Part 1 Satellite imagery: Optical and Radar
1. New algorithms for SBAS and PS and their applications to landslide
2. The use of Optical EO data for monitoring landslide. Recent studies demonstrated the use of Sentinel-2 for landslide activity monitoring in French Alps.
3. The fusion of Optical and Radar EO data
4. SAR applications with Deep Learning - focused on DL (CNN) application to SAR and SAR fusion with Optic applications
5. Deep learning applications to EO data for landslide mapping and monitoring – including landslide feature mapping

Part 2 Drone applications for landslide mapping and monitoring
6. Landslide activity monitoring using optical ultra-high image resolutions; especially the volume and runoff estimations.
7. Thermal drone imagery for mapping surface soil moisture as a proxy for landslide monitoring
8. Deep learning applications for landslide feature mapping
9. Geocoded video for mapping elements exposed to risk induced by a landslide

Part 3 EO data assimilations in landslide susceptibility and hazard mapping
10. Natural hazards mapping and modelling
11. Self-learning algorithms in earth observation applied for landslide mapping and monitoring

Ionu? ?andric is an experienced researcher, with a demonstrated history of working in the computer software industry. Strong information technology professional is skilled in GIS Application, Uncertainty Analysis, Spatial Analysis, Linux System Administration, and Spatial Modeling. He's currently working for the University of Bucharest and Esri Romania. He has been involved in more than 5 Remote Sensing projects focused on Natural Hazards, Soil Moisture, AI and Deep Learning applied in Earth Observation. He is author/co-author so far of +15 peer-reviewed journal articles, +5 book chapters, and editor/co-editor of 1 book. He also has experience in a wide range of +10 research projects funded by international organisations, wherein +5 he has been the PI himself. He is an authorized instructor for Esri and Harris products.
Viorel Ilinca joined the Geological Survey of Romania in 2010, working for the Mapping Department, where its main focus is on landslides research, geomorphological and geological mapping. In the last 14 years, he was involved in several national and international research projects, as well as commercial contracts. As a member of these projects, he focused on various applications of geomorphology and geological mapping for natural hazards assessment. In the field of the geohazard, much of the time is allocated to the study of the debris flow, as well as the landslide mapping.
Zenaida Chitu has an experience of more than ten years in landslide field, developing research projects at the crossroad of geomorphology, engineering geology, and hydrology for improving understanding of landslide occurrence. Her main area of research is related to soil moisture estimation by combining hydrological modelling, remote sensing, and in-situ measurements. She coordinated two national projects as principal investigator and two European projects as a team leader.