Efstratios Karantanellis | Remote Sensing | Best Researcher Award

Efstratios Karantanellis | Remote Sensing | Best Researcher Award

Dr. Efstratios Karantanellis, University of Michigan-Ann Arbor, United States.

Dr. Efstratios Karantanellis is a research fellow in the Department of Earth and Environmental Sciences at the University of Michigan, specializing in natural hazards, engineering geology, and landslide analysis. He obtained his PhD from Aristotle University of Thessaloniki in 2022 and has collaborated on various projects focused on disaster risk reduction and response, utilizing remote sensing and object-based image analysis (OBIA). Efstratios has extensive experience in hazard assessment and mitigation planning, contributing to research in Greece and internationally. He has been recognized with multiple awards for his contributions to the field.ย ๐ŸŒ๐Ÿ”ฌ๐ŸŽ“

Publication Profilesย 

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Education and Experience

  • PhD, Aristotle University of Thessaloniki, Greece (2022)ย ๐ŸŽ“
  • MSc, University of Twente, ITC, Netherlands (2015)ย ๐ŸŒ
  • BSc, Aristotle University of Thessaloniki, Greece (2013)ย ๐Ÿ“š
  • Research Fellow, University of Michigan, Ann Arbor, USA (2022 – ongoing)ย ๐Ÿซ
  • Visiting Researcher, University of California, Berkeley, USA (2024)ย ๐ŸŒ‰
  • Research Associate, various projects in Greece (2020 – 2023)ย ๐Ÿ“Š

Suitability For The Award

Dr. Efstratios Karantanellis is an outstanding candidate for the Best Researcher Award, recognized for his exceptional contributions to geosciences, specifically in the field of disaster risk reduction and environmental management. His extensive educational background, including a PhD from Aristotle University of Thessaloniki and ongoing research at the University of Michigan, equips him with a robust foundation in both theoretical and applied aspects of his discipline.

Professional Development

Dr. Efstratios Karantanellis has actively participated in numerous research projects, enhancing his expertise in engineering geology and disaster risk management. He contributed to the Center for Land Surface Hazards (CLaSH) as part of the U.S. National Science Foundation. His research includes developing tools for landslide disaster risk reduction and coastal zone monitoring systems. By collaborating with interdisciplinary teams, he has leveraged interoperable technologies to support infrastructure resilience. Through his extensive work, Efstratios has made significant contributions to natural hazards research and continues to advance knowledge in this critical field.ย ๐Ÿ”๐Ÿ“ˆ๐Ÿค

Research Focus

Dr. Efstratios Karantanellis focuses on natural hazards, particularly landslide engineering geology and risk management. His research incorporates remote sensing techniques and object-based image analysis (OBIA) to assess and mitigate the impacts of landslides and other geological hazards. He emphasizes disaster risk reduction throughout the disaster life cycle, utilizing innovative methodologies to support effective response and recovery strategies. His work aims to enhance resilience in vulnerable regions, contributing to safer and more sustainable communities. ๐ŸŒช๏ธ๐Ÿž๏ธ๐Ÿงช

Awards and Honors

  • Richard Wolters Prize, International Association for Engineering Geology and the Environment (2024, Runner-up)ย ๐Ÿ†
  • Early Career Research Award of Excellence, Faculty of Natural Sciences, Aristotle University of Thessaloniki (2022)ย ๐ŸŒŸ
  • Postdoctoral Fellowship, NASA’s Applied Science Disasters Program (2022)ย ๐Ÿš€
  • Research Grant, co-financed by Greece and the EU (MIS-5000432)ย ๐Ÿ’ฐ
  • ISPRS Foundation Travel Grant, 2019ย โœˆ๏ธ
  • EuroSDR GeoInformation Travel Grant, 2018ย ๐Ÿ“

Publication Top Notesย 

  • ย  ๐ŸŒ Object-based analysis using UAVs for site-specific landslide assessment – Remote Sensing, 2020, Cited by: 72
  • ๐Ÿ“ก Satellite imagery for rapid detection of liquefaction surface manifestations: Tรผrkiyeโ€“Syria 2023 Earthquakes – Remote Sensing, 2023, Cited by: 32
  • ๐Ÿ“ Automated 3D jointed rock mass structural analysis using LiDAR for rockfall susceptibility – Geotechnical and Geological Engineering, 2020, Cited by: 29
  • ๐Ÿค– Evaluation of machine learning algorithms for object-based mapping of landslide zones using UAV data – Geosciences, 2021, Cited by: 26
  • ๐Ÿ›ฐ๏ธ 3D hazard analysis and object-based characterization of landslide motion using UAV imagery – International Archives of Photogrammetry and Remote Sensing, 2019, Cited by: 20
  • ๐ŸŒช๏ธ The September 18-20 2020 Medicane Ianos Impact on Greece: Phase I Reconnaissance Report – GEER Association, 2020, Cited by: 19ย ย