This paper describes work aimed at consistently labelling surface facets using topographic classes derived from mean and Gaussian curvature measurements. There are two distinct contributions. Firstly, we develop a statistical model which allows label probabilities to be assigned to the di!erent topographic classes. These probabilities capture uncertainties in the computation of surface curvatur...

Agricultural systems are increasingly vulnerable to the effects of extreme climate events. Yet strategies to reduce risk and vulnerability have not been greatly explored. Here, we examine the vulnerability of coffee agroforestry systems varying in management intensity (e.g. land use) and topographic features to disturbance related to Hurricane Stan in Chiapas, Mexico—a hurricane categorized by ...

Topographic patterns are known to affect cellular processes such as adhesion, migration and differentiation. However, the optimal way to deliver topographic signals to provide cells with precise instructions has not been defined yet. In this work, we hypothesize that topographic patterns may be able to control the sensing and adhesion machinery of cells when their interval features are tuned on...

A comparative analysis is presented for empirical topographic normalization of Landsat Thematic Mapper (TM) data in varied forest and topographic settings. The paired study included rugged areas of Glacier National Park, Montana, Linville Gorge Wilderness, North Carolina, and Green Mountains, Vermont, U.S.A. Empirical models of topographic bias achieved significant corrections in the Montana an...

Topology preservation of Self-Organizing Maps (SOMs) is an advantageous property for correct clustering. Among several existing measures of topology violation, this paper studies the Topographic Function (TF) [1]. We find that this measuring method, demonstrated for low-dimensional data in [1], has a reliable foundation in its distance metric for the interpretation of the neighborhood relations...

In this paper, we present a new approach combining topological unsupervised learning with ontology based reasoning to achieve both : (i) automatic interpretation of clustering, and (ii) scaling ontology reasoning over large datasets. The interest of such approach holds on the use of expert knowledge to automate cluster labeling and gives them high level semantics that meets the user interest. T...

The biological function of cortical neurons can often be understood only in the context of large, highly interconnected networks. These networks typically form two-dimensional topographic maps, such as the retinotopic maps in the visual system. Computational simulations of these areas have led to valuable insights about how cortical topography develops and functions, but further progress is dif...

Monitoring urban growth and measuring urban sprawl is essential for improving urban planning and development. In this paper, we introduce a supervised approach for the delineation of urban areas using commonly available topographic data and commercial GIS software. The method uses a supervised parameter optimization approach along with buffer-based quality measuring method. The approach was dev...

Topographic data sets produced by the National Mapping Agencies (NMAs) are an essential part of Spatial Data Infrastructures (SDIs). Both global and European developments will denote that the NMAs will have to adopt new strategies for managing topographic data. This paper will embrace some of the requirements and introduce a framework for topographic data management based on the analysis of stu...