The increasing volume and diversity of sources of geospatial data have created both opportunities and challenges for environmental management studies. Combining and extracting the information contained in these rich multisource data enable novel views and the development of a comprehensive and detailed knowledge basis of the environmental dynamics of both rapidly changing events (e.g. floods) and steady progressions (e.g., crop growth or erosion monitoring). This forum presentation will discuss geospatial data-driven approaches for environmental studies. The approaches are new ways of thinking, enabled by machine learning, to find connections and correlations between environmental variables that traditional model-based approaches might not identify.

Bio

Dr. Leila Hashemi-Beni is an assistant professor of Geomatics and the director of the Geospatial Science and Remote Sensing Laboratory in the Department of Built Environment and College of Science and Technology at North Carolina A&T State University. Her research experience and interests span the areas of multi temporal and multisource data fusion and image classification, 3D data modeling, UAV and satellite remote sensing and data analytics, automatic matching and change detection between various datasets, and developing GIS and remote sensing methodologies for environmental management. She is currently working as a PI/Co-PI on many projects funded by the National Science Foundation (NSF), National Oceanic and Atmospheric Administration (NOAA), North Carolina Collaboratory, North Carolina Department of Transportation (NC DOT), and industry.