12,588 research outputs found

    An Evolutionary Approach to Adaptive Image Analysis for Retrieving and Long-term Monitoring Historical Land Use from Spatiotemporally Heterogeneous Map Sources

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    Land use changes have become a major contributor to the anthropogenic global change. The ongoing dispersion and concentration of the human species, being at their orders unprecedented, have indisputably altered Earth’s surface and atmosphere. The effects are so salient and irreversible that a new geological epoch, following the interglacial Holocene, has been announced: the Anthropocene. While its onset is by some scholars dated back to the Neolithic revolution, it is commonly referred to the late 18th century. The rapid development since the industrial revolution and its implications gave rise to an increasing awareness of the extensive anthropogenic land change and led to an urgent need for sustainable strategies for land use and land management. By preserving of landscape and settlement patterns at discrete points in time, archival geospatial data sources such as remote sensing imagery and historical geotopographic maps, in particular, could give evidence of the dynamic land use change during this crucial period. In this context, this thesis set out to explore the potentials of retrospective geoinformation for monitoring, communicating, modeling and eventually understanding the complex and gradually evolving processes of land cover and land use change. Currently, large amounts of geospatial data sources such as archival maps are being worldwide made online accessible by libraries and national mapping agencies. Despite their abundance and relevance, the usage of historical land use and land cover information in research is still often hindered by the laborious visual interpretation, limiting the temporal and spatial coverage of studies. Thus, the core of the thesis is dedicated to the computational acquisition of geoinformation from archival map sources by means of digital image analysis. Based on a comprehensive review of literature as well as the data and proposed algorithms, two major challenges for long-term retrospective information acquisition and change detection were identified: first, the diversity of geographical entity representations over space and time, and second, the uncertainty inherent to both the data source itself and its utilization for land change detection. To address the former challenge, image segmentation is considered a global non-linear optimization problem. The segmentation methods and parameters are adjusted using a metaheuristic, evolutionary approach. For preserving adaptability in high level image analysis, a hybrid model- and data-driven strategy, combining a knowledge-based and a neural net classifier, is recommended. To address the second challenge, a probabilistic object- and field-based change detection approach for modeling the positional, thematic, and temporal uncertainty adherent to both data and processing, is developed. Experimental results indicate the suitability of the methodology in support of land change monitoring. In conclusion, potentials of application and directions for further research are given

    Study on Semantic Contrast Evaluation Based on Vector and Raster Data Patch Generalization

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    We used buffer superposition, Delaunay triangulation skeleton line, and other methods to achieve the aggregation and amalgamation of the vector data, adopted the method of combining mathematical morphology and cellular automata to achieve the patch generalization of the raster data, and selected the two evaluation elements (namely, semantic consistency and semantic completeness) from the semantic perspective to conduct the contrast evaluation study on the generalization results from the two levels, respectively, namely, land type and map. The study results show that: (1) before and after the generalization, it is easier for the vector data to guarantee the area balance of the patch; the raster data’s aggregation of the small patch is more obvious. (2) Analyzing from the scale of the land type, most of the land use types of the two kinds of generalization result’s semantic consistency is above 0.6; the semantic completeness of all types of land use in raster data is relatively low. (3) Analyzing from the scale of map, the semantic consistency of the generalization results for the two kinds of data is close to 1, while, in the aspect of semantic completeness, the land type deletion situation of the raster data generalization result is more serious

    The Spine of the Cosmic Web

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    We present the SpineWeb framework for the topological analysis of the Cosmic Web and the identification of its walls, filaments and cluster nodes. Based on the watershed segmentation of the cosmic density field, the SpineWeb method invokes the local adjacency properties of the boundaries between the watershed basins to trace the critical points in the density field and the separatrices defined by them. The separatrices are classified into walls and the spine, the network of filaments and nodes in the matter distribution. Testing the method with a heuristic Voronoi model yields outstanding results. Following the discussion of the test results, we apply the SpineWeb method to a set of cosmological N-body simulations. The latter illustrates the potential for studying the structure and dynamics of the Cosmic Web.Comment: Accepted for publication HIGH-RES version: http://skysrv.pha.jhu.edu/~miguel/SpineWeb

    Network Centralities in Polycentric Urban Regions: Methods for the Measurement of Spatial Metrics

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    The primary aim of this thesis is to explain the complex spatial organisations of polycentric urban regions (PURs). PURs are a form of regional morphology that often evolves from post-industrial structures and describe a subnational area featuring a plurality of urban centres. As of today, the analysis of the spatial organisation of PURs constitutes a hitherto uncharted territory. This is due to PURs’ inherent complexity that poses challenges for their conceptualisation. In this context, this thesis reviews theories on the spatial organisation of regions and cities and seeks to make a foundational methodological contribution by joining space syntax and central place theory in the conceptualisation of polycentric urban regions. It takes into account human agency embedded in the physical space, as well as the reciprocal effect of the spatial organisation for the emergence of centralities and demonstrates how these concepts can give insights into the fundamental regional functioning. The thesis scrutinises the role that the spatial organisation plays in such regions, in terms of organising flows of goods and people, ordering locational occupation and fostering centres of commercial activity. It proposes a series of novel measurements and techniques to analyse large and messy datasets. This includes a method for the application of large-scale volunteered geographic information in street network analysis. This is done, in the context of two post-industrial regions: the German Ruhr Valley and the British Nottinghamshire, Derbyshire and Yorkshire region. The thesis’ contribution to the understanding of regional spatial organisation and the study of regional morphology lies in the identification of spatial structural features of socio-economic potentials of regions and particular areas within them. It constitutes the first comparative study of comprehensive large-scale regional spatial networks and presents a framework for the analysis of regions and the evaluation of the predictive potential of spatial networks for socio-economic patterns and the location of centres in regional contexts

    Proceedings of Workshop on New developments in Space Syntax software

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    Methodology and Algorithms for Pedestrian Network Construction

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    With the advanced capabilities of mobile devices and the success of car navigation systems, interest in pedestrian navigation systems is on the rise. A critical component of any navigation system is a map database which represents a network (e.g., road networks in car navigation systems) and supports key functionality such as map display, geocoding, and routing. Road networks, mainly due to the popularity of car navigation systems, are well defined and publicly available. However, in pedestrian navigation systems, as well as other applications including urban planning and physical activities studies, road networks do not adequately represent the paths that pedestrians usually travel. Currently, there are no techniques to automatically construct pedestrian networks, impeding research and development of applications requiring pedestrian data. This coupled with the increased demand for pedestrian networks is the prime motivation for this dissertation which is focused on development of a methodology and algorithms that can construct pedestrian networks automatically. A methodology, which involves three independent approaches, network buffering (using existing road networks), collaborative mapping (using GPS traces collected by volunteers), and image processing (using high-resolution satellite and laser imageries) was developed. Experiments were conducted to evaluate the pedestrian networks constructed by these approaches with a pedestrian network baseline as a ground truth. The results of the experiments indicate that these three approaches, while differing in complexity and outcome, are viable for automatically constructing pedestrian networks
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