Confluence Retirement

In an effort to consolidate USGS hosted Wikis, the myUSGS Confluence service is targeted for retirement on January 28, 2022. The official USGS Wiki and collaboration space is now SharePoint. Please migrate existing spaces and content to the SharePoint platform and remove it from Confluence at your earliest convenience. If you need any additional information or have any concerns about this change, please contact myusgs@usgs.gov. Thank you for your prompt attention to this matter.

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Title: Turning nature into numbers: Advancing Ecoscience through the lens of remote sensing and artificial intelligence/machine learning

Recording: On Microsoft Stream for Dept of Interior employees

Abstract: The EcoAI project supports USGS science initiatives within dryland and high-latitude ecosystems through the integration of field observations, remote sensing, and artificial intelligence/machine learning (AI/ML). Dr. Neal Pastick will discuss how EcoAI makes use of cloud and high-performance computing resources (e.g., USGS HPCs, Amazon Web Services, Google Cloud Platform) and AI/ML to solve complex environmental problems. Specifically, this talk will focus on the USGS Rangeland Exotic Plant Monitoring System that was developed to fingerprint the abundance, spread, and impacts of invasive plants in the western United States. This system currently leverages Harmonized Landsat and Sentinel-2 data and machine and deep learning techniques to assess the past, present, and future abundance of exotic annual grasses and the resistance of sagebrush ecosystems to invasion. If time allows, Dr. Pastick will also present research aimed at better understanding high-latitude ecosystems and the development of a global lake monitoring system for assessing changes in surface area, color, and temperature using Alaska as a pilot study area.

Bios: Neal Pastick is a Research Physical Scientist with the U.S. Geological Survey (USGS) – Earth Resources Observation and Science (EROS) Center and a Ph.D. graduate from the Natural Resources Science and Management Program at the University of Minnesota – Twin Cities.  He has conducted remote sensing research for environmental science applications over the past 11 years, with an emphasis on characterizing changing aquatic and terrestrial landscapes through the assimilation of field studies, remote sensing, and AI/ML. 

2021 March 9 - Integrating Deep Learning Methods into a National High-Resolution Land Cover Mapping

Recording:

Title: Integrating Deep Learning Methods into a National High-Resolution Land Cover Mapping

Nate Herold is a Physical Scientist with NOAA's Office for Coastal Management (OCM) where he manages the Coastal Change Analysis Program (C-CAP), NOAA's national land cover mapping and monitoring effort. He has been with OCM for 18 years, is intimately involved with Data and Tool offerings within our Digital Coast site, and is currently the chair of the Multi-Resolution Land Characteristics (MRLC) Consortium, helping to coordinate the land cover mapping work from all major federal agencies. 

Chris Robinson is a Senior Remote Sensing Analyst with LynkerTechnologies on contract at the NOAA Office for Coastal Management (OCM) where he serves as the lead technical analyst for the Coastal Change Analysis Program (C-CAP). Chris has over 20 years of experience working with high-resolution imagery for the development of coastal land cover and habitat data.

2021 February 9 - Assessing the Artificial Intelligence (AI) Readiness of USGS Data

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