Santosh Panda

Dr. Panda holds a Ph.D. in Geology from University of Alaska Fairbanks. His research is focused on advancing the application of remote sensing to improve scientific understanding of Arctic landscape change and related geo-hazards in Alaska. As an Adjunct Teaching Faculty he has taught several upper level undergraduate classes and graduate classes in remote sensing and GIS at the University of Alaska Fairbanks.

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GIS Image Analysis in ArcGIS Pro (edX) EdX
University of Alaska Fairbanks,AlaskaX

GIS Image Analysis in ArcGIS Pro (edX)

Dive into the world of Geographic Information Systems (GIS) with our specialized course on Image Analysis in ArcGIS Pro. This edX offering provides a thorough grounding in remote sensing techniques, image processing methodologies, and their practical applications to solve complex environmental and geographical challenges. Whether you're interested in understanding landscape changes or tackling sophisticated problems like facial recognition, this course equips you with the skills needed to analyze images effectively.

Self Paced
Self-Paced
Remote Sensing of Wildfires (edX) EdX
University of Alaska Fairbanks,AlaskaX

Remote Sensing of Wildfires (edX)

Explore the fascinating world of wildfire detection and management with our Remote Sensing of Wildfires course. Delve into the use of cutting-edge satellite technology and remote sensing science to monitor and analyze wildfires. Gain insights into how these techniques help manage fire spread, support ecological restoration efforts, and understand the natural role of wildfires in ecosystems.

Self Paced
Self-Paced
GIS Foundations (edX) EdX
University of Alaska Fairbanks,AlaskaX

GIS Foundations (edX)

Discover the fundamentals of Geographic Information Systems (GIS) with our beginner-friendly course. Gain a deep understanding of GIS concepts and geospatial technology to unlock new ways of viewing, analyzing, and interpreting geographical data. Perfect for those looking to solve complex problems and make informed decisions based on spatial data.

Self Paced
Self-Paced
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