Dr. Sergii Skakun is an Assistant Professor with a joint appointment at the Department of Geographical Sciences and the College of Information Studies (iSchool). He joined UMD in October 2015. From 2013 to 2015, he was a Senior Engineer at Samsung SDI (South Korea), where he was responsible for developing industrial vision inspection systems. From 2006 to 2013, he has held multiple positions (latest Senior Scientist) at the Space Research Institute (Ukraine), where he was performing research in remote sensing. He received PhD in Computer Science from National Academy of Sciences of Ukraine in 2005.
Dr. Skakun has participated as a PI or Co-I in projects funded by NASA, Google, European Commission (EC), EC Joint Research Center (JRC), U.S. Civilian Research & Development Foundation (CRDF), National Academy of Sciences of Ukraine, State Space Agency of Ukraine and Science & Technology Center in Ukraine (STCU). He is currently a PI on several NASA funded projects, including “Crop yield assessment and mapping by a combined use of Landsat-8, Sentinel-2 and Sentinel-1 images”, “Maintenance and refinement of the Suomi NPP VIIRS Land Surface Reflectance product suite”, and “Open-Source Deep Learning Classification and Visualization of Multi-Temporal Multi-Source Satellite Data”. From 2005 to 2013, he was a member of the Working Group on Information Systems and Services (WGISS) of the Committee of Earth Observation Satellites (CEOS), where he was involved on developing the SensorWeb technology for disaster monitoring and risk assessment. He is currently co-leading a CEOS Cloud Masking Inter-comparison Exercise. He is currently an Associate Editor for the journal AIMS Geosciences (section: Computing Sciences for Environment) and Section Editorial Board Member for the journal Remote Sensing (section: Remote Sensing Image Processing). As of now, he authored or co-authored 49 papers in peer-reviewed journals, 3 books (in Ukrainian/Russian), and 4 chapters in edited books.
His current research focus is to advance methods, models and emerging technologies in the area of data science for heterogeneous remote sensing data fusion, processing and analysis, and their applications to Earth System Science and areas of societal benefit.
Areas of Interest
- Remote sensing
- Agricultural monitoring
- Machine learning in remote sensing
- Disaster monitoring and risk assessment
Degree TypePhDDegree DetailsComputer Science, Space Research Institute NASU-SSAU (Ukraine), 2005
Degree TypeMSDegree DetailsApplied Mathematics, National Technical University of Ukraine “Kyiv Polytechnic Institute”, 2004
Degree TypeBSDegree DetailsApplied Mathematics, National Technical University of Ukraine “Kyiv Polytechnic Institute”, 2002
ProfessionalAssociate Editor of the journal AIMS Geosciences
InternationalTask Coordinator of the Cloud Masking Inter-comparison eXercise (CMIX) within CEOS WGCV
CampusMember of the Symposia Working Group of the UMD Year of Data Science (YoDS) Initiative
ProfessionalEditorial Board Member, section “Remote Sensing Image Processing”, journal Remote Sensing