Natural resource inventories are more and more based on quantitative spatial methods. The discrete classes of vegetation, soil, land cover etc. can be quantified by using membership maps rather than only crisp delineations. GIS methods to visually explore fuzziness and uncertainty of the prediction maps are clearly needed. The paper presents two relatively new techniques for visualisation of mu...

The European indoor Radon map which is currently under production is based on gridded data supplied by the contributing countries. Each grid node represents the arithmetic mean (among other statistics) of the individual measurements within 10 x 10 km(2), called cells, pixels or blocks, which are aligned to a common metric coordinate system. During work the question emerged, if uncertainty in th...

Pest risk maps can provide useful decision support in invasive species management, but most do not adequately consider the uncertainty associated with predicted risk values. This study explores how increased uncertainty in a risk model's numeric assumptions might affect the resultant risk map. We used a spatial stochastic model, integrating components for entry, establishment, and spread, to es...

This paper demonstrates an improved robot localisation approach based on adaptive sensing. Using a particle filter to represent the uncertainty in location the approach minimises the expected entropy after the next robot action. It is demonstrated in simulation that this approach is useful in sensor management of an Autonomous Underwater Vehicle when a digitised elevation map of the environment...

Figure 1. Diffusion tensor ellipsoid map. Figure 2. Higher order covariance visualization. 4388 Diffusion Tensor Uncertainty: Visualization and Similarity Metrics Mustafa Okan Irfanoglu, Michael Curry, Evren Özarslan, Cheng Guan Koay, Sinisa Pajevic, and Peter J. Basser NIH, NICHD, Bethesda, MD, United States, Center for Neuroscience and Regenerative Medicine, Uniformed Services University of t...

This paper presents an approach to learning from noisy data that views the problem as one of reasoning under uncertainty, where prior knowledge of the noise process is applied to compute a pos-teriori probabilities over the hypothesis space. In preliminary experiments this maximum a posteri-ori (MAP) approach exhibits a learning rate advantage over the C4.5 algorithm that is statistically signi...

Background: Cross-discipline team collaboration between the project ownership team, design team and project delivery team is central to effective management of risk, uncertainty and ambiguity. A recently-developed framework that was developed to provide a visualisation tool to enable various project procurement and delivery forms has been adapted to answer the research question How can uncertai...