Geology topics

Xiran Zhou

Publications and source records attributed to Xiran Zhou.

3 recordsLinked to original sources

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 ↗