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OpenTopography Receives Funding Renewal from the National Science Foundation

The National Science Foundation (NSF) has renewed funding for OpenTopography, a science gateway that provides online access to Earth science oriented high resolution topography data and processing tools to advance understanding of the Earth’s surface, vegetation, and built environment. OpenTopography is a comprehensive platform for open access to high resolution and global topographic data that is used by an international community of researchers, educators, government agencies, industry, and hobbyists.

OpenTopography is a collaborative project operated by the San Diego Supercomputer Center (SDSC) at the University of California San Diego, EarthScope Consortium, and Arizona State University’s (ASU) School of Earth and Space Exploration (SESE).

The award “Sustained Resources: OpenTopography - An AI-ready Cyberinfrastructure Facility for Advancing Our Understanding of a Changing Earth” provides $4.18 million over four years for the fifth generation of the project. The award is funded by the Geoinformatics program in NSF’s Division of Earth Sciences and the National Discovery Cloud for Climate (NDC-C) initiative of NSF.

Under the new award, OpenTopography continues to advance efforts to ease topographic data discovery, access, processing, and visualization for use primarily in education and research.

Read all the details in this SDSC press release: OpenTopography Receives $4 Million to Support AI-Ready Access to Topographic Data for Research and Education

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topographic differencing of a landslide in Big Sur, CA with eroded areas shown in red and areas of deposition shown in blue
The 2017 Mud Creek landslide occurred along the Big Sur coastline of central California. The red areas indicate zones of erosion or material loss, while blue areas highlight where the displaced material was deposited.
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lidar hillshade with yellow and purple overlain colors delineating mapped sand dunes and bedrock
Machine learning and high resolution topography are used in geologic mapping to leverage the expertise of geologists and streamline repetitive steps in the mapping process. This image demonstrates the effectiveness of general AI models in distinguishing between bedrock (purple) and sand dunes (tan).