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

Exploring possibilities of including detailed ALS derived biomass information into physically-based slope stability models at regional scale

Digital surface model, hillshade and orthophoto of the world's largest fossil oyster reef, links to GeoTIFFs

(1) Dept. of Physical Geography, Cath. University of Eichstaett-Ingolstadt, Germany (tobias.heckmann@ku-eichstaett.de, ++49 (0)8421 931787), (2) Institute for Geosciences and Geography, University of Halle-Wittenberg, Germany, (3) Dept. of Applied Geology, University of Erlangen-Nuremberg, Germany, (4) Dept. of Geomorphology and Soil Science, Technical University of Munich, Weihenstephan, Germany, (5) Institute of Meteorology and Geophysics, University of Innsbruck, Austria, (6) Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Austria

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Quantifying proglacial morphodynamics and sediment budgets - the PROSA approach

(1) Christian Doppler Laboratory for Spatial Data from Laser Scanning and Remote Sensing, Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Austria, (2) Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Austria, (3) Department of Geophysics and Space Science, Eotvos University, Budapest, Hungary, (4) Department of Geodynamics and Sedimentology, University of Vienna, Austria, (5) Landesvermessungsamt Vorarlberg, Feldkirch, Austria

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Effect of the acquisition geometry of Airborne and Terrestrial Laser Scanning on high-resolution outlining of microtopographic landforms

. Nowadays, point clouds are the standard product when capturing reality independent of scale and measurement technique. Especially, Dense Image Matching (DIM) and Laser Scanning (LS) are state of the art capturing methods for a great variety of applications producing detailed point clouds up to billions of points. In-depth analysis of such huge point clouds typically requires sophisticated spatial indexing structures to support potentially long-lasting automated non-interactive processing tasks like feature extraction, semantic labelling, surface generation, and the like. Nevertheless, a visual inspection of the point data is often necessary to obtain an impression of the scene, roughly check for completeness, quality, and outlier rates of the captured data in advance. Also intermediate processing results, containing additional per-point computed attributes, may require visual analyses to draw conclusions or to parameterize further processing. Over the last decades a variety of commercial, free, and open source viewers have been developed that can visualise huge point clouds and colorize them based on available attributes. However, they have either a poor loading and navigation performance, visualize only a subset of the points, or require the creation of spatial indexing structures in advance. In this paper, we evaluate a progressive method that is capable of rendering any point cloud that fits in GPU memory in real time without the need of time consuming hierarchical acceleration structure generation. In combination with our multi-threaded LAS and LAZ loaders, we achieve load performance of up to 20 million points per second, display points already while loading, support flexible switching between different attributes, and rendering up to one billion points with visually appealing navigation behaviour. Furthermore, loading times of different data sets for different open source and commercial software packages are analysed.

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Norbert Pfeifer

Papers

Exploring possibilities of including detailed ALS derived biomass information into physically-based slope stability models at regional scale

Digital surface model, hillshade and orthophoto of the world's largest fossil oyster reef, links to GeoTIFFs

Quantifying proglacial morphodynamics and sediment budgets - the PROSA approach

Effect of the acquisition geometry of Airborne and Terrestrial Laser Scanning on high-resolution outlining of microtopographic landforms

Efficient loading and visualization of massive feature-rich point clouds without hierarchical acceleration structures