We report on recent developments in the European INTAMAP (Interoperability and Automated Mapping) project on web services for processing radiological data from the EURDEP data base. First, we briefly describe the goals of this project and its relation to other European initiatives and infrastructures for global monitoring of the environment (GMES and INSPIRE). Then we report on the development ...

Spatial information surveyed by photogrammetry, airborne LiDAR and Mobile Measurement System (MMS) above ground level can be analyzed by scientists using standard geostatistical methodologies such as ordinary Kriging and sequential Gaussian simulation to interpolate heterogeneities of profiles from sparse sample data. Proven effective by researchers, the Kriging algorithm model is used by comme...

Kernel-based non-parametric models have been applied widely over recent years. However, the associated computational complexity imposes limitations on the applicability of those methods to problems with large data-sets. In this paper we develop a filtering approach based on a Gaussian process regression model. The idea is to generate a smalldimensional set of filtered data that keeps a high pro...

This paper presents AutoDJ: a system for automatically generating music playlists based on one or more seed songs selected by a user. AutoDJ uses Gaussian Process Regression to learn a user preference function over songs. This function takes music metadata as inputs. This paper further introduces Kernel Meta-Training, which is a method of learning a Gaussian Process kernel from a distribution o...

In this paper we shall discuss an extension to Gaussian process (GP) regression models, where the measurements are modeled as linear functionals of the underlying GP and the estimation objective is a general linear operator of the process. We shall show how this framework can be used for modeling physical processes involved in measurement of the GP and for encoding physical prior information in...

A classification strategy which does not require a priori assumptions about the statistical distribution of training pixels in each class is proposed. This method uses an indicator kriging approach in feature space to classify remotely sensed images incorporating both spectral and textural information of bands. Texture information is used only in cases where spectral information is not sufficie...

This paper demonstrates the application of correlated Gaussian process based approximations to optimization where multiple levels of analysis are available, using an extension to the geostatistical method of co-kriging. An exchange algorithm is used to choose which points of the search space to sample within each level of analysis. The derivation of the co-kriging equations is presented in an i...

In this study, the adaptive Kriging controller is investigated for the longitudinal dynamics of a generic hypersonic flight vehicle. For the altitude subsystem, the dynamics are transformed into the strict-feedback form where the back-stepping scheme is used. The velocity subsystem is transformed into the output-feedback form. Considering the non-linearity of the dynamics, the nominal feedback ...

Kriging (Gaussian process, spatial correlation) metamodels approximate the Input/Output (I/O) functions implied by the underlying simulation models; such metamodels serve sensitivity analysis and optimization, especially for computationally expensive simulations. In practice, simulation analysts often know that the I/O function is monotonic. To obtain a Kriging metamodel that preserves this kno...