An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States |
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| Authors: | Jochen Wendel Barbara P Buttenfield Lawrence V Stanislawski |
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| Institution: | 1. European Institute for Energy Research (EIFER), Karlsruhe Institute of Technology, Karlsruhe, Germany;2. Department of Geography, University of Colorado, Boulder, CO, USA;3. Center for Excellence in Geospatial Information Science (CEGIS), United States Geological Survey (USGS), Rolla, MO, USA |
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| Abstract: | Knowledge of landscape type can inform cartographic generalization of hydrographic features, because landscape characteristics provide an important geographic context that affects variation in channel geometry, flow pattern, and network configuration. Landscape types are characterized by expansive spatial gradients, lacking abrupt changes between adjacent classes; and as having a limited number of outliers that might confound classification. The US Geological Survey (USGS) is exploring methods to automate generalization of features in the National Hydrography Data set (NHD), to associate specific sequences of processing operations and parameters with specific landscape characteristics, thus obviating manual selection of a unique processing strategy for every NHD watershed unit. A chronology of methods to delineate physiographic regions for the United States is described, including a recent maximum likelihood classification based on seven input variables. This research compares unsupervised and supervised algorithms applied to these seven input variables, to evaluate and possibly refine the recent classification. Evaluation metrics for unsupervised methods include the Davies–Bouldin index, the Silhouette index, and the Dunn index as well as quantization and topographic error metrics. Cross validation and misclassification rate analysis are used to evaluate supervised classification methods. The paper reports the comparative analysis and its impact on the selection of landscape regions. The compared solutions show problems in areas of high landscape diversity. There is some indication that additional input variables, additional classes, or more sophisticated methods can refine the existing classification. |
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| Keywords: | Automated generalization unsupervised clustering supervised classification landscape regions |
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