PhD Defence Mr Xiang Zhang

Department of Geo-Information Processing

Title of defence

Automated evaluation of generalized topographic maps

—supported by formalization and data enrichment techniques

To meet the increasing demand for geospatial information at multiple scales, and to shorten update cycles for up-to-date geo-data, national mapping agencies (NMAs) and other data providers have introduced, or are considering the use of, (semi-)automated generalization systems in their production lines. Map generalization inevitably changes certain aspects (accuracy, completeness, etc.) of spatial data. Therefore, generalized data/maps should be evaluated to see whether they are in line with the map requirements defined for a target scale and for a certain task (e.g. visualization). The use of (semi-)automated generalization systems, on the other hand, increases the need for automated approaches to quality evaluation. This is because automated evaluation techniques provide more objective evaluation results and reduce the time needed for the subjective visual assessment.

This thesis has identified the several issues that have not been well addressed by previous studies:

• Lack of formalized map requirements: cartographic constraints were specified at a knowledge level that is only human readable, thus not suitable for automated evaluation. For example, complex concepts, relationships, and acceptable modifications described in the constraints are not always well defined.
• Lack of balanced map requirements for a meaningful evaluation: previous research focused more on readability constraints and constraints for one object, or between two objects. The evaluation based on unbalanced constraints may lead to misleading (e.g. over-optimistic) results.
• Lack of explicit information/knowledge, such as characteristics, relationships and patterns in topographic data, and links between corresponding entities between different scales.

The main research question of this thesis is therefore “how to evaluate the quality of generalized general-purpose topographic data in a (semi-)automatic way, so that a meaningful evaluation can be reached?”

To begin with, I have proposed a three-step evaluation framework consisting of data enrichment, data matching and evaluation (Fig. 1).

 
Fig. 1 A conceptual framework for automated evaluation of generalization output

In addition, this thesis has presented a first-order representation for multi-scale data that formalizes concepts of different granularities (i.e. objects, groups and feature classes) and complex relationships (e.g. semantic and contextual ones). Cartographic constraints can be precisely defined on top of this formal theory. For example, the constraint ‘area of any target polygon should be larger than a certain threshold’ can be formalized as:
 

Later in a design and implementation phase, I have focused on specific problems in automatically evaluating preservation constraints and the constraints on group level and feature class level. I have used building and road feature classes to demonstrate the proposed three-step evaluation approach, where settlement structures (e.g. building patterns) and spatial distributions of feature classes are extensively explored.

This thesis contributes to the specific problems in the following ways:

• I have proposed an improved approach to building pattern recognition in topographic data, where different pattern types recognized from different algorithms can be combined to obtain a better recognition (e.g. Fig. 2);
  

                 

Fig. 2 Building features in a topographic dataset (a) and building patterns recognized

• I have proposed a generic matching approach based on pattern classification idea where various criteria can be aggregated to make decisions; besides, I have devised ad-hoc algorithms to match building patterns and alignments at different scales;
• I have proposed an approach to evaluate building alignments in generalized data, making use of the results obtained in the above steps;
• I have made attempts to measure spatial distributions of feature classes; two approaches are promising in comparing the similarity of spatial distributions: one is based on local densities and the other is based on distance.

To conclude, the formalization of map specifications, data enrichment and data matching techniques form a promising framework and provide necessary instruments for automated evaluation of generalization output. This has been demonstrated by evaluating building alignments in generalized maps. Fig. 3 gives an example where building patterns are recognized, matched and evaluated.

Fig. 3 Building alignments (a type of building pattern) are evaluated in terms of alignment orientation (ODev indicates the deviation of their orientations, the smaller, the better these alignments are generalized)

Since this research does not provide a full solution to automated evaluation, a comprehensive evaluation integrating more constraints and feature classes for a whole map is a topic for further research.

Biography

Zhang, Xiang was born on the 26th of November 1982 in Hubei province, China. He studied at the
School of Resource and Environmental Sciences, Wuhan University, China from 2001 to 2005, where he obtained his Bachelor degree on geoinformatics and cartography. Later he continued his study towards an M.Sc. on map generalization and multi-scale representations at Wuhan University. Since 2008 he has carried out a doctoral study at Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, the Netherlands. His Ph.D. topic is on the quality issues in map generalization and multi-scale data. His research interest includes multi-scale data modeling, knowledge formalization, and application of computational geometry and pattern recognition in automated map generalization and evaluation.

Xiang Zhang, Kraak, M.J. (Promotor) and Molenaar, M. (Promotor)  (2012) Automated evaluation of generalized topographic maps. Enschede, University of Twente Faculty of Geo-Information and Earth Observation (ITC), 2012. ITC Dissertation 213, ISBN: 978-90-6164-338-8

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