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CSS leads USGS’s mission as the civilian mapping agency for the Nation. We conduct detailed surveys and develop high quality, highly accurate topographic, geologic, hydrographic, and biogeographic maps and data. Our maps allow precise planning for critical mineral assessments; energy development; infrastructure projects; urban planning; flood prediction; emergency response; and hazard mitigation.

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National Geospatial Program

National Geospatial Program

The National Geospatial Program is the Federal civilian mapping agency and provides the digital geospatial foundation for the Nation. It engages partners and communities of use to collaboratively produce consistent and accurate topographic map data.

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Science Analytics and Synthesis (SAS)

Science Analytics and Synthesis (SAS)

The USGS Science Data Catalog, supported by SAS, provides seamless access to USGS research and monitoring data from across the nation. Users have the ability to search, browse, or use a map-based interface to discover data.

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News

Date published: January 23, 2020

FINISHED!! TNMCorps Mapping Challenge for City / Town Hall Structures in UT, NM, & AZ

This challenge is now FINISHED!!  Thank you to everyone who participated!

Date published: January 21, 2020

TNMCorps Mapping Challenge Summary Results for City / Town Hall Structures in MI

This challenge is now complete!!  Thank you to everyone who contributed!  Here is a time lapse of our volunteer contributions for this challenge:

Date published: January 9, 2020

NEW Mapping Challenge for City/Town Halls in Washington and Oregon!

Topo maps will soon be updated for western states, so here’s a new challenge for city/town halls in Washington and Oregon!

Publications

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Year Published: 2019

Employing an ecosystem services framework to deliver decision ready science

Public land managers have limited information to allow for the integration and balancing of multiple objectives in land management decisions including the social (cultural and health), economic (monetary and nonmonetary), and environmental aspects. In this article, we document an approach to consider the many facets of decision making by...

Pindilli, Emily J.; Hogan, Dianna M.; Zhu, Zhiliang

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Year Published: 2019

A spatio-contextual probabilistic model for extracting linear features in hilly terrain from high-resolution DEM data

This paper introduces our research in developing a probabilistic model to extract linear terrain features from high resolution DEM (Digital Elevation Model) data. The proposed model takes full advantage of spatio-contextual information to characterize terrain changes. It first derives a quantifiable measure of spatio-contextual patterns of linear...

Zhou, Xiran; Li, Wenwen; Arundel, Samantha

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Year Published: 2019

The effect of resolution on terrain feature extraction

Recent increase in the production of high-resolution digital elevation models (DEMs) from lidar data has led to interest in their use for terrain mapping. Although the impact of different resolutions has been studied relative to terrain characteristics like roughness, slope and curvature, its relationship to the extraction of terrain features...

Arundel, Samantha; Li, Wenwen; Zhou, Xiran