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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 remains unclear. To address this question, this study t
Authors
Samantha Arundel, Wenwen Li, Xiran Zhou

U.S. Geological Survey accomplishments in cartography 2015-2019

The U.S. Geological Survey (USGS), the United States' official national topographic mapping organization, is building and maintaining geographic databases for fundamental base geographic layers of land cover, structures, boundaries, hydrography, geographic names, transportation, elevation, and orthoimagery as The National Map. Data from the 3D Elevation Program, the National Hydrography Dataset an
Authors
E. Lynn Usery

Semantically supported linked data mapping

Semantic technology based on the Resource Description Framework (RDF) modeling environment has introduced new data management capabilities that can lead to innovative cartographic techniques. This report describes research toward more semantically expressive linked geospatial data mapping, topics of research, and an avenue for further  international collaboration.
Authors
Dalia E. Varanka

Simplification of polylines by segment collapse: Minimizing areal displacement while preserving area

This paper reports on a new Area Preserving Segment Collapse (APSC) algorithm for simplifying polygonal boundaries while preserving polygonal area at simplified target scales and minimizing areal displacement. A general segment collapse algorithm is defined by iteratively collapsing segments to Steiner points in priority order, guided by placement and displacement functions. The algorithm is speci
Authors
Barry J. Kronenfeld, Larry Stanislawski, Barbara P. Buttenfield, Tyler Brockmeyer

Supply chain infrastructure restoration calculator software tool—Developer guide and user manual

This report describes a software tool that calculates costs associated with the reconstruction of supply chain interdependent critical infrastructure in the advent of a catastrophic failure by either outside forces (extreme events) or internal forces (fatigue). This tool fills a gap between search and recover strategies of the Federal Emergency Management Agency (or FEMA) and construction techniqu
Authors
Akhilesh Ojha, Bhanu Kanwar, Suzanna K. Long, Thomas G. Shoberg, Steven Corns

Problems of Large Spatial Databases

Large spatial databases often labeled as geospatial big data exceed the capacity of commonly used computing systems as a result of data volume, variety, velocity, and veracity. Additional problems also labeled with V’s are cited, but the four primary ones are the most problematic and focus of this chapter (Li et al., 2016, Panimalar et al., 2017).  Sources include satellites, aircraft and drone pl
Authors
E. Lynn Usery

Topographic mapping evolution: From field and photogrammetric data collection to GIS production and Linked Open Data

Whither the topographic map? Topographic mapping historically has been approached as a map factory operation through the period 1879-1990. During this time, data were field and photogrammetrically collected; cartographically verified and annotated creating a compilation manuscript; further edited, generalized, symbolized, and produced as a graphic output product using lithography, or more recently
Authors
E. Lynn Usery, Dalia E. Varanka, Larry Davis

Generalization in practice within national mapping agencies

National Mapping Agencies (NMAs) are still among the main end users of research into automated generalisation, which is transferred into their produc- tion lines via various means. This chapter includes contributions from seven NMAs, illustrating how automated generalisation is used in practice within their partly or fully automated databases and maps production lines, what results are currently b
Authors
Cécile Duchêne, Blanca Baella, Cynthia A. Brewer, Dirk Burghardt, Barbara P. Buttenfield, Julien Gaffuri, Dominik Käuferle, Francois Lecordix, Emmanuel Maugeais, Ron Nijhuis, Maria Pla, Marc Post, Nicolas Regnauld, Larry Stanislawski, Jantien Stoter, Katalin Tóth, Sabine Urbanke, Vincent van Altena, Antje Wiedemann

Deep convolutional neural networks for map-type classification

Maps are an important medium that enable people to comprehensively understand the configuration of cultural activities and natural elements over different times and places. Although a massive number of maps are available in the digital era, how to effectively and accurately locate and access the desired map on the Internet remains a challenge today. Previous works partially related to map-type cla
Authors
Xiran Zhou, Wenwen Li, Samantha Arundel, Jun Liu

Streams do work: Measuring the work of low-order streams on the landscape using point clouds

The mutable nature of low-order streams makes regular updating of surface water maps necessary for accurate representation. Low-order streams make up roughly half the streams in the conterminous United States by length, and small inaccuracies in stream head location can result in significant error in stream reach, order, and density. Reliable maps of stream features are vital for hydrologic modeli
Authors
Ethan J. Shavers, Larry V. Stanislawski

Validating the use of object-based image analysis to map commonly-recognized landform features in the United States

The U.S. Geological Survey (USGS) National Geospatial Program (NGP) seeks to i) create semantically-accessible terrain features from the pixel-based 3D Elevation Program (3DEP) data, and ii) enhance the usability of the USGS Geographic Names Information System (GNIS) by associating boundaries with GNIS features whose spatial representation is currently limited to 2D point locations. Geographic obj
Authors
Samantha Arundel, Gaurav Sinha

Automated road breaching to enhance extraction of natural drainage networks from elevation models through deep learning

High-resolution (HR) digital elevation models (DEMs), such as those at resolutions of 1 and 3 meters, have increasingly become more widely available, along with lidar point cloud data. In a natural environment, a detailed surface water drainage network can be extracted from a HR DEM using flow-direction and flow-accumulation modeling. However, elevation details captured in HR DEMs, such as roads a
Authors
Larry Stanislawski, Tyler Brockmeyer, Ethan J. Shavers