Sentence examples for spatial classifications from inspiring English sources

The estimates from the mixed effects spline model can be readily incorporated into secondary analyses, for instance, targeting spatial classifications of brain regions according to their modeled response profiles.

Spatial classification based on the field soil bearing capacity values is used in applying this function.

Based on these analyses, eight regional classifications of rural urban interfaces were proposed and evaluated based on different spatial classification methods and clustering techniques.

The maps generated have been validated and evaluated for checking the consistency in spatial classification of susceptibility zones using prediction rate curve, landslide density and error matrix methods.

A spatial classification shows that between 9% and 55% of the population was concealed by landscape elements that reduce image contrast.

Accurate and timely spatial classification of crop types based on remote sensing data is important for both scientific and practical purposes.

Regional rural urban classifications from the eight spatial classification methods (part I of this article) and 2010 national census were validated using three rural urban water indicators from random sampling of the rural urban household survey in the CMR.

We describe a supervised spatial classification algorithm in which the primary goal is to stratify a landscape or other spatial data into a set of discrete patches based on available categorical and continuous data.

Pattern-based regionalization – spatial classification of an image into sub-regions characterized by relatively stationary patterns of pixel values – is of significant interest for conservation, planing, as well as for academic research.

The resulting FT record showed mean annual spatial classification accuracies of 91 (+/−8.6) and 84 (+/−9.3) percent for PM and AM overpass retrievals relative to in situ air temperature measurements from the global weather station network.

Even though the overall accuracy of the correlation matrix of the NDVI image, the spectral and the spatial classification in the subsets ranged from 87% to 95%, land cover still might be misclassified to some extent.