Statistics for Spatial Data.

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Climate change 2014 - Synthesis Report

We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearest-neighbors, partial least squares and principal component regression, logistic and multinomial regression, multiple adaptive regression splines and other methods), implemented in Weka, R (with and without the caret package), C and Matlab, including all the relevant classifiers available today. We use 121 data sets, which represent the whole UCI data base (excluding the large-scale problems) and other own real problems, in order to achieve significant conclusions about the classifier behavior, not dependent on the data set collection. The classifiers most likely to be the bests are the random forest (RF) versions, the best of which (implemented in R and accessed via caret) achieves 94.1% of the maximum accuracy overcoming 90% in the 84.3% of the data sets. However, the difference is not statistically significant with the second best, the SVM with Gaussian kernel implemented in C using LibSVM, which achieves 92.3% of the maximum accuracy. A few models are clearly better than the remaining ones: random forest, SVM with Gaussian and polynomial kernels, extreme learning machine with Gaussian kernel, C5.0 and avNNet (a committee of multi-layer perceptrons implemented in R with the caret package). The random forest is clearly the best family of classifiers (3 out of 5 bests classifiers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in the top-20, respectively).

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Do we need hundreds of classifiers to solve real world classification problems

A wide range of methods for analysis of airborne- and satellite-derived imagery continues to be proposed and assessed. In this paper, we review remote sensing implementations of support vector machines (SVMs), a promising machine learning methodology. This review is timely due to the exponentially increasing number of works published in recent years. SVMs are particularly appealing in the remote sensing field due to their ability to generalize well even with limited training samples, a common limitation for remote sensing applications. However, they also suffer from parameter assignment issues that can significantly affect obtained results. A summary of empirical results is provided for various applications of over one hundred published works (as of April, 2010). It is our hope that this survey will provide guidelines for future applications of SVMs and possible areas of algorithm enhancement.

https://www.aboutgis.com/Publications/Mountrakis_SVM_review_in_remote_sensing_ISPRS2010.pdf

Support vector machines in remote sensing: A review

(1) Balaton Limnological Institute, Centre for Ecological Research, Hungarian Academy of Sciences, Tihany, Hungary (azlinszky@gmail.com), (2) Department of Geodesy and Geoinformation, Vienna University of Technology, Vienna, Austria ([philipp.glira;norbert.pfeifer]@geo.tuwien.ac.at), (3) Centre of Geodetic Earth System Research, Deutsches Geodätisches Forschungsinstitut, München, Germany (eva.boergens@dgfi.badw.de)

Comparing airborne LIDAR water surface heights with synchronous Envisat altimetry over Lake Balaton, Hungary

. Modern data acquisition with active or passive photogrammetric imaging techniques generally results in 3D point clouds. Depending on the acquisition or processing method, the spacing of the individual points is either uniform or irregular. In the latter case, the neighbourhood definition like for digital images (4- or 8-neighbourhood, etc.) cannot be applied. Instead, analysis requires a local point neighbourhood. The local point neighbourhood with conventional k-nearest neighbour or fixed distance searches often produce sub-optimal results suffering from the inhomogeneous point distribution. In this article, we generalize the neighbourhood definition and present a generic spatial search framework which explicitly deals with arbitrary point patterns and aims at optimizing local point selection for specific processing tasks like interpolation, surface normal estimation and point feature extraction, spatial segmentation, and such like. The framework provides atomic 2D and 3D search strategies, (i) k-nearest neighbour, (ii) region query, (iii) cell based selection, and (iv) quadrant/octant based selection. It allows to freely combine the individual strategies to form complex, conditional search queries as well as specifically tailored point sub-selection. The benefits of such a comprehensive neighbourhood search approach are showcased for feature extraction and surface interpolation of irregularly distributed points.

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A framework for generic spatial search in 3d point clouds

. The geomorphology of loess plateaus continuously evolves with gully erosion. It is very hard to simulate geomorphic evolution in history, especially considering longer time spans, because of the lack of ancient geographic information. However, historical documents and archaeological sites are providing indispensable information about the past world. Using the remains of Wucheng in the Shanxi Province in China as the study area, we explored the possibility of combining UAV (unmanned aerial vehicle) photogrammetry and GNSS (global navigation satellite system) measurements for modelling current terrain as well as ancient terrain with the help of archaeological surveying. Finally, current and ancient topography were modelled and the soil erosion was assessed. We believe our work can provide a clear workflow for terrain modelling in archaeological sites and may offer new ideas for landform evolution studies.

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Terrain modelling based on archaeological remains in the loess plateau of china

Surface Roughness from Point Clouds - A Multi-Scale Analysis

In order to study and analyse the recent changes in a proglacial environment the multi-disciplinary research project PROSA (high-resolution measurements of morphodynamics in rapidly changing PROglacial Systems of the Alps) selected the study area of the Gepatschferner (Tyrol), the second largest glacier in Austria. One of the challenges within the project is the geometric integration (i.e. georeferencing) of multi-temporal topographic data sets in a continuously changing environment. Furthermore, one has to deal with data sets of multiple scales (large area data sets vs. highly detailed local area observations) that are on one hand necessary to cover the complete proglacial area with the whole catchment and on the other hand guaranty a highly dense and accurate sampling of individual areas of interest (e.g. a certain highly affected slope).

Norbert Pfeifer

Papers

Comparing airborne LIDAR water surface heights with synchronous Envisat altimetry over Lake Balaton, Hungary

A framework for generic spatial search in 3d point clouds

Terrain modelling based on archaeological remains in the loess plateau of china

Surface Roughness from Point Clouds - A Multi-Scale Analysis

Integration of multi-temporal airborne and terrestrial laser scanning data for the analysis and modelling of proglacial geomorphodynamic processes