Geology topics

Samantha T. Arundel

Publications and source records attributed to Samantha T. Arundel.

30 records · Page 2Linked to original sources

USGS2020

GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning

Machine learning allows “the machine” to deduce the complex and sometimes unrecognized rules governing spatial systems, particularly topographic mapping, by exposing it to the end product. Often, the obstacle to this approach is the acquisition of many good and labeled training examples of the desired result. Such is the case with most types of natural features. To address such limitations, this research introduces GeoNat v1.0, a natural feature dataset, used to support artificial intelligence‐based mapping and automated detection of natural features under a supervised learning paradigm. The dataset was created by randomly selecting points from the U.S. Geological Survey’s Geographic Names Information System and includes approximately 200 examples each of 10 classes of natural features. Resulting data were tested in an object‐detection problem using a region‐based convolutional neural network. The object‐detection tests resulted in a 62% mean average precision as baseline results. Major challenges in developing training data in the geospatial domain, such as scale and geographical representativeness, are addressed in this article. We hope that the resulting dataset will be useful for a variety of applications and shed light on training data collection and labeling in the geospatial artificial intelligence domain.

Transactions in GIS↗

USGS2020

Automated location correction and spot height generation for named summits in the coterminous United States

Spot elevations published on historical U.S. Geological Survey topographic maps were established as needed to enhance information imparted by the quadrangle’s contours. In addition to other features, labels were routinely placed on mountain summits. While some elevations were established through field survey triangulation, many were computed during photogrammetric stereo-compilation. Today, Global Navigation Satellite System (GNSS) receivers have replaced expensive triangulation methods. However, since GNSS measurements require visiting the feature location, a national dataset containing high-accuracy spot elevations has not yet been created. Consequently, modern U.S. Topo maps are devoid of mountain peak or other spot elevations. Still, topographic map users continue to demand the display of spot heights. Therefore, a pilot study was conducted to evaluate the feasibility of automatically generating elevation values at named U.S. summits using available elevation data. The devised method uses an uphill stepping technique to find the most likely highest point in subsequently higher-resolution elevation models. Resulting elevation values are compared to other published sources. Results from 196 summits indicate that values derived from lidar are generally higher, whereas those populated from the one-third arc-second USGS Seamless 3DEP elevation dataset are generally lower. A thorough understanding of these relationships require the evaluation of more points.

International Journal of Digital Earth↗

USGS2020

Automated extraction of areal extents for GNIS Summit features using the eminence core method

An important objective of the U.S. Geological Survey (USGS) is to enhance the Geographic Names Information System (GNIS) by automatically associating boundaries with terrain features that are currently spatially represented as two-dimensional points. In this paper, the discussion focuses on experiments for mapping GNIS Summit features using the eminence core region-growing method, which maps the area between a peak and its key col (saddle). A secondary goal of this project is to improve the positional accuracy of GNIS Summit features, since those locations were derived long ago and need to be snapped to local morphometric peaks detected from analysis of the highest-resolution digital elevation models (DEMs). The eminence cores delineated for a subset of GNIS Summit features were compared visually against basemaps and manually digitized polygons created by USGS staff. The comparisons revealed substantial differences between the computationally derived eminence cores and the manually generated polygons. Results clearly suggest that the default core delineation method tested must be modified to “roll back” or truncate growth of unreasonably large cores to smaller extents that would match people’s intuitive expectations. However, these results are far more encouraging than any method tested previously, since this method guarantees a 1-1 correspondence between polygons and GNIS Summit features.

Conference Paper↗

USGS2020

Improving the positional and vertical accuracy of named summits above 13,000 ft in the United States

The National Map (TNM) portal provides public access to U.S. Geological Survey (USGS) high-resolution topographic datasets, and maps from the Historical Topographic Map Collection (HTMC). Elevation values shown on HTMC maps were obtained from ground spot elevation measurements, as compared to today’s elevation measurements derived from more efficient methods, such as lidar, radar, or sonar. These spot elevations were collected either by levelling in the field or by photogrammetrists in the office, and are called mass points with post-spacings of two-arc seconds (arcsec), approximately 60 meters depending on latitude, in steep terrain and one-half arcsec, approximately 15 meters, in flat terrain (Federal Geographic Data Committee 1997). The vertical accuracy of spot elevations is ± 10 feet. Most spot elevations were used only in contour derivation to create a more spatially continuous representations of terrain, but some were also labelled on the maps to supply accurate elevations of culturally important features such as mountain peaks, gaps, and road junctions.

Conference Paper↗

USGS2019

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 tests the impact of four resolutions on the capture of glacial cirques from DEMs. Mean curvature was derived from one arc-second, one-third arc-second, one-ninth arc-second and half meter DEMs representing a cirque-covered mountainous region southwest of Lake Tahoe, California. Using a GEOBIA workflow, ridge objects were identified, and three scales - via the multi-resolution scale parameter (SP) - of objects bordering the ridges were classified as cirque objects. The resulting classifications were compared to reference cirques digitized at a scale of ~1:10,000. Results show that the one-third arc-second DEM produces the set of cirque objects most closely resembling the reference cirques. The one-ninth arc-second DEM afforded the second-best classification. These results emphasize the importance in carefully choosing resolution relative to the features extracted, rather than using the highest resolution data available. In the case of GEOBIA workflows, the choice of scale parameter is equally important.

California↗

USGS2019

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 classification mainly focused on map comparison and map matching at the local scale. The features derived from local map areas might be insufficient to characterize map content. To facilitate establishing an automatic approach for accessing the needed map, this paper reports our investigation into using deep learning techniques to recognize seven types of map, including topographic, terrain, physical, urban scene, the National Map, 3D, nighttime, orthophoto, and land cover classification. Experimental results show that the state-of-the-art deep convolutional neural networks can support automatic map-type classification. Additionally, the classification accuracy varies according to different map-types. This work can contribute to the implementation of deep learning techniques in the cartographic community and advance the progress of Geographical Artificial Intelligence (GeoAI).

Conference Paper↗

USGS2018

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 terrain feature, such as ridgelines, valley lines and crater boundaries, and then adopts multiple neighborhood analysis and a probability model to address data uncertainty in terrain surface modeling. Different from traditional approaches, the proposed model has the ability to achieve near-automated processing, and to support effective extraction of terrain features in both smooth and rough surfaces. Through a series of experiments, we demonstrate that the proposed approach outperforms existing techniques, including: thresholding, stream/drainage network analysis, visual descriptor, object-based image analysis and edge detection. We hope this work contributes to both the geospatial data science and geomorphology communities with a new way of utilizing high-resolution imagery in terrain analysis.

International Journal of Geographical Information ↗

USGS2018

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 object-based image analysis (GEOBIA) was determined to be a promising method to approach both goals. An existing GEOBIA workflow was modified and the resulting segmented objects and terrain categories tested for a strategically chosen physiographic province in the mid-western US, the Ozark Plateaus. The chi-squared test of independence confirmed that there is significant overall spatial association between terrain categories of the GEOBIA and GNIS feature classes. Contingency table analysis also suggests strong category-specific associations between select GNIS and GEOBIA classes. However, 3D visual analysis revealed that GEOBIA objects resembled segmented regions more than they did individual landform objects, with their boundaries often failing to correspond to match what people would likely perceive as landforms. Still, objects derived through GEOBIA can provide initial baseline landscape divisions that can improve the efficiency of more specialized feature extraction methods.

Cartography and Geographic Information Science↗

USGS2018

The National Elevation Dataset

The National Elevation Dataset (NED) is a primary elevation data product that has been produced and distributed by the U.S. Geological Survey (USGS). Since its inception, the USGS has compiled and published topographic information in many forms, and the NED is a significant development in this long line of products that describe the land surface. The NED provides seamless raster elevation data of the conterminous United States (CONUS), Alaska, Hawaii, U.S. island territories, Mexico, and Canada. The NED is derived from diverse source datasets that are processed to a specification with consistent resolutions, coordinate system, elevation units, and horizontal and vertical datums. The NED was developed as the logical result of the maturation of the long-standing USGS elevation program, which for many years concentrated on production of quadrangle-based digital elevation models (DEM). The NED contributes to the elevation layer of The National Map, and it provides basic elevation information for earth science studies and mapping applications in the U.S. and most of North America. For over 15 years (1999–2014), the NED served as the flagship elevation product of the USGS. In 2015, the 3D Elevation Program (3DEP) was initiated. When the 3DEP initiative became operational, the name “National Elevation Dataset” (and the abbreviation “NED”) were retired as the USGS elevation activities and data were rebranded under the 3DEP banner. However, elevation data produced and distributed as part of the NED are still widely used (and distributed by other entities), so there is a continuing need for detailed documentation, including how it was produced, its accuracy, and how it is used. This chapter directly addresses that need for detailed information about the NED. The most recent detailed description of the NED appeared in the 2nd edition of the DEM Users Manual (2007), and because NED production continued through 2014, the details reported herein provide valuable information for data accessed by the user community from 2007 through 2014. The NED has been widely used in operational applications and research studies and is extensively cited in reports on those activities, so it is important for the user community to have access to information about the NED to better judge how its qualities and characteristics might affect results derived from its use as the elevation data source. Additionally, the NED seamless layers serve as one of the input data sources for the current 3DEP elevation production system, so, as with any input data source, an understanding of the data characteristics is critical.

Book chapter↗

USGS2017

A reference landform ontology for automated delineation of depression landforms from DEMs

Abstract The landform reference ontology (LFRO) is being developed to formalize ontological distinctions underlying naïve geographic cognition and reasoning about landforms. The LFRO taxonomy is currently based only on form-based distinctions. In this significantly revised version, several new categories have been added to explicate ontological distinctions related to material-spatial dependence and physical support. Nuances of common natural language landform terms and implications for their mapping are discussed.

Conference Paper↗

USGS2017

The National Map seamless digital elevation model specifications

This specification documents the requirements and standards used to produce the seamless elevation layers for The National Map of the United States. Seamless elevation data are available for the conterminous United States, Hawaii, Alaska, and the U.S. territories, in three different resolutions—1/3-arc-second, 1-arc-second, and 2-arc-second. These specifications include requirements and standards information about source data requirements, spatial reference system, distribution tiling schemes, horizontal resolution, vertical accuracy, digital elevation model surface treatment, georeferencing, data source and tile dates, distribution and supporting file formats, void areas, metadata, spatial metadata, and quality assurance and control.

Techniques and Methods↗

USGS2015

1-Meter Digital Elevation Model specification

In January 2015, the U.S. Geological Survey National Geospatial Technical Operations Center began producing the 1-Meter Digital Elevation Model data product. This new product was developed to provide high resolution bare-earth digital elevation models from light detection and ranging (lidar) elevation data and other elevation data collected over the conterminous United States (lower 48 States), Hawaii, and potentially Alaska and the U.S. territories. The 1-Meter Digital Elevation Model consists of hydroflattened, topographic bare-earth raster digital elevation models, with a 1-meter x 1-meter cell size, and is available in 10,000-meter x 10,000-meter square blocks with a 6-meter overlap. This report details the specifications required for the production of the 1-Meter Digital Elevation Model.

Techniques and Methods↗