Catálogo de publicaciones - libros

Recent progress in remote sensing has made massive amounts of high resolution terrain data readily available. Often the data is distributed as regular grid terrain models where each grid cell is associated with a height. When terrain analysis applications process such massive terrain models, data movement between main memory and slow disk (), rather than CPU time, often becomes the performance bottleneck. Thus it is important to consider I/O-efficient algorithms for fundamental terrain problems. One such problem is the hierarchical decomposition of a grid terrain model into —regions where all water flows towards a single common outlet. Several different hierarchical watershed decompositions schemes have been described in the hydrology literature. One important such scheme is the label method where each watershed is assigned a unique label and each grid cell is assigned a sequence of labels corresponding to the (nested) watersheds to which it belongs.

In this paper we present an I/O-efficient algorithm for computing the Pfafstetter label of each cell of a grid terrain model. The algorithm uses ( rt()) I/Os, the number of I/Os needed to sort elements, where is the total length of the cell labels. To our knowledge, our algorithm is the first efficient algorithm for the problem. We also present the results of a experimental study using massive real life terrain data that shows our algorithm is practically as well as theoretically efficient.

This paper demonstrates a toolkit for multiple observer siting to maximize their joint viewshed, on high-resolution gridded terrains, up to 2402 × 2402, with the viewsheds’ radii of up to 1000. It shows that approximate (rather than exact) visibility indexes of observers are sufficient for siting multiple observers. It also shows that, when selecting potential observers, geographic dispersion is more important than maximum estimated visibility, and it quantifies this. Applications of optimal multiple observer siting include radio towers, terrain observation, and mitigation of environmental visual nuisances.

In order to compute lines of constant gradient and areas of constant aspect on a terrain, we introduce the notion of scale dependent local gradient and aspect for a neighborhood around each point of a terrain. We present three definitions for local gradient and aspect, and give efficient algorithms to compute them. We have implemented our algorithms for grid data and we compare the results for all methods.

Current land use patterns with low-density, single-use, and leapfrogging urban growth on city outskirts call for more efficient land use development strategies balancing economy, environmental protection, and social equity. In this paper, we present a new spatial multiobjective optimization model with a constraint based on the level of neighborhood development density. The constraint encourages infill development and land use compatibility by requiring compact and contiguous land use allocation. The multiobjective optimization model presented in this paper minimizes the conflicting objectives of open space development, infill and redevelopment, land use neighborhood compatibility, and cost distance to already urbanized areas.

Topographic features such as physical objects become more complex due to increasing multiple land use. Increasing awareness of the importance of sustainable (urban) development leads to the need for 3D planning and analysis. As a result, topographic products need to be extended into the third dimension. In this paper, we developed a new topological 3D data model that relies on Poincaré algebra. The internal structure is based on a network of simplexes, which are well defined, and very suitable for keeping the 3D data set consistent. More complex 3D features are based on this simple structure and computed when needed. We describe an implementation of this 3D model on a commercial DBMS. We also show how a 2D visualizer can be extended to visualize these 3D objects.

Many decisions — not only in the field of Emergency Management or Military oriented actions — require nowadays in addition to reaching verdicts a large amount of spatial and geographical information data. If these data are handled in Geographical Information Systems — GIS, we are introducing new possibilities to handle and analyze this type of information in a way that divert substantially from traditional handling of the paper maps. A Geographical Information System is an IS with the capabilities not only to handle currently being produced digital maps in raster and vector formats but in addition analyze those for instance together with Remote Sensing techniques like GPS positioning and combining it with a real time intelligence reports. The development of the societies parallel to globalization and global dependencies trends, some symptoms of climate changes, ageing population, more complex societies and more complex systems lead also to a grater demand for more sophisticated information and information systems (Trnka 2003; Trnka et al. 2005a and 2005b; Quarantelli 1999; Rubin 1998; Rubin 2000; Kiranoudis et al. 2002; Mendonça et al. 2001; Beroggi 2001; Johnson 2002). The research teams at IDA/LiU have extensive experience with testing various forms of data capture, real time analyzes and diffusion of geographically registered data through, for example, mobile GIS technology.

However, we have experienced a necessity for development of both entirely GIS-based models and supportive to them data to be analyzed, in order to improve all crucial phases of the Emergency Management scenario reasoning during a preventive and as well an information provisions stages. In this way information could be regarded as a strategic infrastructure that is now being investigated on the Swedish national level as well as by the European Union, for example through the European Network of Excellence — the GMOSS. One goal for GMOSS is to investigate good procedures for Emergency Management and Crisis Response, and as a consequence to build standardized and harmonized geographical databases that can be used in decision support systems. What is still to be added to the agenda is an implementation of several GIS-based models that are making use of all those databases for prediction of potential hazards, for preventive works and action plans. Objectives in this article are to contribute to the development of such models and to investigate necessary data for use in rescue-, relief- and preventive works, and to stress obligations for data uptodateness for structuralizing better preparedness plans etc.