This paper presents a survey of georeferenced point clouds. Concentration is, on the one hand, put on features, which originate in the measurement process themselves, and features derived by processing the point cloud. On the other hand, approaches for the processing of georeferenced point clouds are reviewed. This includes the data structures, but also spatial processing concepts. We suggest a categorization of features into levels that reflect the amount of processing. Point clouds are found across many disciplines, which is reflected in the versatility of the literature suggesting specific features.

Making sense of the physical world has always been at the core of mapping. Up until recently, this has always dependent on using the human eye. Using airborne lasers, it has become possible to quickly "see" more of the world in many more dimensions. The resulting enormous point clouds serve as data sources for applications far beyond the original mapping purposes ranging from flooding protection and forestry to threat mitigation. In order to process these large quantities of data, novel methods are required. In this contribution, we develop models to automatically classify ground cover and soil types. Using the logic of machine learning, we critically review the advantages of supervised and unsupervised methods. Focusing on decision trees, we improve accuracy by including beam vector components and using a genetic algorithm. We find that our approach delivers consistently high quality classifications, surpassing classical methods.

This paper is about simulating the spread of opinions in a society and about finding ways to counteract that spread. To abstract away from potentially emotionally laden opinions, we instead simulate the spread of a zombie outbreak in a society. The virus causing this outbreak is different from traditional approaches: it not only causes a binary outcome (healthy vs infected) but rather a continuous outcome. To counteract the outbreak, a discrete number of infection-level specific treatments is available. This corresponds to acts of mild persuasion or the threats of legal action in the opinion spreading use case. This paper offers a genetic and a cultural algorithm that find the optimal mixture of treatments during the run of the simulation. They are assessed in a number of different scenarios. It is shown, that albeit far from being perfect, the cultural algorithm delivers superior performance at lower computational expense.

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In this paper, we apply genetic algorithms to the field of electoral studies. Forecasting election results is one of the most exciting and demanding tasks in the area of market research, especially due to the fact that decisions have to be made within seconds on live television. We show that the proposed method outperforms currently applied approaches and thereby provide an argument to tighten the intersection between computer science and social science, especially political science, further. We scrutinize the performance of our algorithm's runtime behavior to evaluate its applicability in the field. Numerical results with real data from a local election in the Austrian province of Styria from 2010 substantiate the applicability of the proposed approach.