CAREER: Mapping Anthropocene Geomorphology with Deep Learning, Big Data Spatial Analytics, and LiDAR

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Geospatial data (e.g., aerial and satellite imagery, digital elevation data, and weather observations) are being generated at an astounding rate. One example, the US Geological Survey’s Landsat Earth observation program, collects about a terabyte of data daily, and their 3D Elevation Program (3DEP) is working toward generating the first-ever high-detail elevation dataset for the entire country, which is scheduled to be completed by 2023. While these “big data” projects present many opportunities, it is currently very difficult to extract actionable information from them in an efficient manner that supports scientific research and informed decision making. While recent advances in artificial intelligence and machine learning show great promise in analyzing such data, there is a need to further research and develop these techniques for application to digital mapping tasks, such as detecting landslides, monitoring resource extraction, and documenting landscape change. This research will develop state-of-the-art “deep learning”-based techniques to derive valuable information on human modifications to the landscape using geospatial data, including elevation models and historic maps, to fundamentally advance geomorphic mapping science. In addition to supporting and training graduate students at West Virginia University, the work will engage future high school STEM teachers, in-service teachers, and high school students by developing training and instructional materials that will help enable the next generation of data scientists, geospatial professionals, and coders.This project will advance the application of geospatial data analytics and advanced computational methods to extract high spatial resolution information from geospatial data over wide regions to further understanding of natural landscapes and anthropogenic landscape change. It specifically explores semantic and instance segmentation deep learning methods based on convolutional neural networks (CNNs), which are able to model spatial context information, for extracting geomorphic features and historic mining from geospatial data, including historic topographic maps, light-detection and ranging (LiDAR) point clouds, and additional terrain representations (i.e., hillshades and other topographic derivatives). Ultimately, this project will contribute to operationalizing deep learning for geomorphic mapping using the increasing abundance of quality digital terrain data with the eventual goal of generating accurate datasets at regional to global extents that will allow for documentation, quantification, and modeling of geomorphic hazards and natural and anthropogenic landscape change. This project is jointly funded by Geomorphology and Land-use Dynamics and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.