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. 🌍🔬🎓

Publication Profiles 

Googlescholar

Education and Experience

  • PhD, Aristotle University of Thessaloniki, Greece (2022) 🎓
  • MSc, University of Twente, ITC, Netherlands (2015) 🌍
  • BSc, Aristotle University of Thessaloniki, Greece (2013) 📚
  • Research Fellow, University of Michigan, Ann Arbor, USA (2022 – ongoing) 🏫
  • Visiting Researcher, University of California, Berkeley, USA (2024) 🌉
  • Research Associate, various projects in Greece (2020 – 2023) 📊

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. 🔍📈🤝

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. 🌪️🏞️🧪

Awards and Honors

  • Richard Wolters Prize, International Association for Engineering Geology and the Environment (2024, Runner-up) 🏆
  • Early Career Research Award of Excellence, Faculty of Natural Sciences, Aristotle University of Thessaloniki (2022) 🌟
  • Postdoctoral Fellowship, NASA’s Applied Science Disasters Program (2022) 🚀
  • Research Grant, co-financed by Greece and the EU (MIS-5000432) 💰
  • ISPRS Foundation Travel Grant, 2019 ✈️
  • EuroSDR GeoInformation Travel Grant, 2018 📍

Publication Top Notes 

  •   🌍 Object-based analysis using UAVs for site-specific landslide assessment – Remote Sensing, 2020, Cited by: 72
  • 📡 Satellite imagery for rapid detection of liquefaction surface manifestations: Türkiye–Syria 2023 Earthquakes – Remote Sensing, 2023, Cited by: 32
  • 📏 Automated 3D jointed rock mass structural analysis using LiDAR for rockfall susceptibility – Geotechnical and Geological Engineering, 2020, Cited by: 29
  • 🤖 Evaluation of machine learning algorithms for object-based mapping of landslide zones using UAV data – Geosciences, 2021, Cited by: 26
  • 🛰️ 3D hazard analysis and object-based characterization of landslide motion using UAV imagery – International Archives of Photogrammetry and Remote Sensing, 2019, Cited by: 20
  • 🌪️ The September 18-20 2020 Medicane Ianos Impact on Greece: Phase I Reconnaissance Report – GEER Association, 2020, Cited by: 19  

Mr. Mohammad Marjani | Remote sensing | Best Researcher Award

Mr. Mohammad Marjani | Remote sensing | Best Researcher Award 

Mr. Mohammad Marjani, Memorial University of Newfoundland, Canada

Mohammad Marjani is a dedicated researcher and educator currently pursuing a Doctor of Philosophy in Electrical and Computer Engineering at Memorial University of Newfoundland, specializing in advanced remote sensing and deep learning algorithms for environmental monitoring under the supervision of Dr. Masoud Mahdianpari. He holds a Master of Science in Geospatial Information System (GIS) from K.N.Toosi University of Technology, where he graduated with a stellar GPA of 4.0/4.0, focusing on wildfire spread modeling using deep learning techniques. His academic journey began with a Bachelor of Science in Geodesy and Geomatic Engineering from the same university, where he researched 3D change detection methods in point clouds.Marjani’s research interests span deep learning, machine learning, spatio-temporal modeling, and remote sensing, with particular emphasis on natural hazards like wildfires and methane monitoring. He has accumulated valuable teaching experience as a Teaching Assistant at both the Iran National Geographical Organization and K.N.Toosi University, imparting knowledge in image processing, MATLAB, and Python programming.In addition to his academic endeavors, Marjani is a co-founder of GeoHoosh, an educational group dedicated to promoting artificial intelligence in geomatic and geospatial engineering. His commitment to advancing the field through both research and education underscores his role as a rising expert in geospatial technologies and environmental monitoring.

 

Professional Profile

🎓 EDUCATION

Doctor of Philosophy, Electrical and Computer Engineering
📅 Sep 2023 – Present
📍 Memorial University of Newfoundland, St. John’s, NL, Canada
🌐 Advanced remote sensing and deep learning algorithms for environment monitoring
👨‍🏫 Supervisor: Dr. Masoud Mahdianpari

Master of Science, Geospatial Information System (GIS)
📅 Sep 2020 – Nov 2022
📍 K.N.Toosi University of Technology, Tehran, Iran (KNTU)
📊 GPA: 18.58/20 (4.0/4.0)
🔥 The wildfire spread modeling using deep learning techniques
👨‍🏫 Supervisor: Dr. M.S. Mesgari

Bachelor of Science, Geodesy and Geomatic Engineering
📅 Sep 2016 – Sep 2020
📍 K.N.Toosi University of Technology, Tehran, Iran (KNTU)
📊 GPA: 16.22/20 (3.34/4.0)
📐 Thesis Title: Evaluation of 3D change detection methods in point clouds
👨‍🏫 Supervisor: Dr. H. Ebadi

🔬 RESEARCH INTERESTS

  • Deep Learning 🧠
  • Machine Learning 🤖
  • Spatio-temporal Modeling 🌍
  • Wildfire 🔥
  • Remote Sensing 🛰️
  • Natural Hazards 🌪️
  • Wetland Monitoring 🌿
  • Methane Monitoring 🌱

💼 EXPERIENCE

Teaching Assistantships, Faculty of Iran National Geographical Organization
🖥️ Image Processing
📅 Sep 2019 – Jan 2020

  • Taught MATLAB programming language 💻
  • Prepared lectures 📝
  • Graded course assessments 🧾
  • Defined assignments 📚

Teaching Assistantships, K.N.Toosi University of Technology
🖥️ Computational Intelligence
📅 Sep 2022 – Jan 2023

  • Taught Python programming language 🐍
  • Prepared lectures 📝
  • Graded course assessments 🧾
  • Defined assignments 📚

Co-Founder of GeoHoosh
🌐 Educational Group
📅 Sep 2023 – Present

  • One of the four founders of GeoIntelligence Education Group, named GeoHoosh in Persian 🇮🇷
  • Aims to educate Artificial Intelligence in the Geomatic/Geospatial engineering sub-fields 🧭

Publications Notes:📄

Application of Explainable Artificial Intelligence in Predicting Wildfire Spread: An ASPP-Enabled CNN Approach

CNN-BiLSTM: A Novel Deep Learning Model for Near-Real-Time Daily Wildfire Spread Prediction