Deep Learning and Remote Sensing for Coastal Resilience: Rapid and Automated Coastal Monitoring to Inform Community Recovery from Storm Events (2019-2020)
Remote sensing plays a critical role in the study and management of coastal ecosystems. Unoccupied aerial systems (UAS, also known as drones) complement satellites and are becoming an integral tool in studying and managing coastal systems, providing on-demand remote sensing capabilities at low cost and with modular sensors that can assess a wide variety of variables. While powerful tools, satellites and UAS generate massive amounts of data. The expertise and time required to analyze this imagery is an obstacle in using it to inform disaster response and storm recovery – an obstacle machine learning is well poised to mitigate.
This project team developed a methodology for long-term monitoring of land cover dynamics in the coastal Southeast United States. They did so by leveraging deep learning to automate classification of Landsat 5 satellite imagery into discrete land cover types (e.g. wetland, agriculture, forest). This land cover data continues to be analyzed to better understand long term changes (e.g. wetland migration, urban expansion) and impacts of specific events, namely hurricanes.
In addition to increasing understanding of land cover change across the North Carolina Coastal Plain, the change detected in this analysis will guide future drone surveys for areas that experienced substantial change. The team prototyped this “tip-and-cue” system by targeting drone flights at the Nags Head Woods Preserve based on changes detected via satellite.
Spring 2019 – Fall 2019
Justin T. Ridge, David W. Johnston. 2020. “Unoccupied Aircraft Systems (UAS) for Marine Ecosystem Restoration,” Frontiers in Marine Science 7:438
Nags Head Woods Preserve: An Application of Remote Sensing to Analyze Land Cover Change and Ecosystem Response (ArcGIS StoryMap by Kendall Jefferys)
Justin T. Ridge, Patrick C. Gray, Anna E. Windle, David W. Johnston. “Deep Learning for Coastal Resource Conservation: Automating Detection of Shellfish Reefs.” 2020. Remote Sensing in Ecology and Conservation.
Creating Custom Loss Functions for Multiclass Classification (poster by Yousuf Rehman)
Deep Learning for Land Cover Classification (poster by Diego Chamorro)
Deep Learning and Remote Sensing for Costal Resilience: Scikit-Learn Model Performance (poster by Sofia Nieto)
NLCD Analysis of Wetland and Tree Canopy Dynamics in Eastern North Carolina (poster by Kendall Jefferys)
This Team in the News
- Patrick Gray, Nicholas School of the Environment-Marine Science and Conservation-PHD
- David Johnston, Nicholas School of the Environment-Marine Science and Conservation
- Justin Ridge, Nicholas School of the Environment-Marine Science and Conservation
/undergraduate Team Members
Diego Chamorro, Electrical & Computer Egr(BSE), Computer Science (BSE2)
Kendall Jefferys, Environmental Sci/Policy (AB), English (AB2)
Feroze Mohideen, Electrical & Computer Egr(BSE), Computer Science (BSE2)
Sofia Nieto, Interdepartmental Major
Yousuf Rehman, Electrical & Computer Egr(BSE)
/yfaculty/staff Team Members
Guillermo Sapiro, Pratt School of Engineering-Electrical & Computer Engineering
Brian Silliman, Nicholas School of the Environment-Marine Science and Conservation
/zcommunity Team Members
Brian Boutin, The Nature Conservancy
Carolyn Currin, National Oceanic and Atmospheric Administration (NOAA)
Jenny Davis, National Oceanic and Atmospheric Administration (NOAA)
Paula Gilikin, North Carolina Division of Coastal Management
Aaron McCall, The Nature Conservancy
Brandon Puckett, North Carolina Division of Coastal Management
Sarah Spiegler, North Carolina Sentinel Site Cooperative