Author: Qi, Huabin
Title: Detection and generalization of changes in settlements for automated digital map updating
Degree: Ph.D.
Year: 2009
Subject: Hong Kong Polytechnic University -- Dissertations.
Topographic maps.
Topographic maps -- Data processing.
Department: Department of Land Surveying and Geo-Informatics
Pages: 165 leaves : ill. (some col.) ; 30 cm.
Language: English
Abstract: Nowadays, updating and maintenance of topographic maps at various scales have become difficult tasks faced by National Mapping Agencies (NMAs). Traditionally, maps at each scale were updated independently. Such a procedure is labor intensive, time consuming and costly. A more promising method is to frequently update larger-scale maps first, and then to update smaller-scale maps by detecting and generalizing changes from the already-updated larger-scale maps. This study focuses on the second step of the method, with settlements as an example. The aim is to develop some methods for automated detection and generalization of changes between 1:10 000 and 1:50 000 scale maps. For detection of changes between two topographic maps, most of existing methods were developed for maps at similar scales. They did not take into consideration the differences in representations on two maps at different scales. In this study, a method for detection of changes between already-updated larger-scale (1:10 000) map and to-be-updated smaller-scale (1:50 000) map is developed. In the method, geographic data matching technique is employed to detect and recognize discrepancies between maps. Causes of these discrepancies are categorized into three types, namely data error, multiple representation and actual change. Since it is difficult to precisely tell whether a discrepancy is effected by certain particular type of cause, the method emphasizes the final "updates" which need to be applied to smaller-scale map instead of "actual changes". To realize this idea, discrepancies are quantified and represented in a formalized way and a series of rules are formulated to identify updates from discrepancies by combining cause-effect analysis and quantification of discrepancies. An experimental test has also been carried out to evaluate the method. The test results suggest that this method works well in the present scale range. Generalization of detected changes in settlements usually consists of two consecutive steps, namely building grouping and generalization execution. This study mainly focuses on the first step, aiming at developing a more generic method. To do so, contextual features and three Gestalt factors, namely proximity, common orientation and similarity, are identified as constraints for building grouping and degree of proximity, difference of orientations and degree of similarity are separately used to describe the three Gestalt factors. As for the use of the Gestalt constraints in building grouping process, two possible modes, namely parallel mode and hierarchical mode, are explored. First, an attempt to group buildings based on parallel use of Gestalt constraints is made, in which a minimum spanning tree (MST) is used to link all buildings into a group and these three Gestalt factors are integrated into a mathematical model which is then used as edge cost of the MST to partition the group into subgroups. Experimental tests suggest that parallel use of Gestalt constraints is not capable of producing satisfactory results for building grouping in an automated way. Then, a psychophysical test on how the three Gestalt constraints are used in manual building grouping process was conducted. Test results indicate that there is a hierarchical relationship among their uses. After that, a method for building grouping based on a hierarchical use of constraints is developed. In the method, proximity is first used to partition buildings in a region into different groups. Then for those groups in which the degree of proximity among buildings is medium, orientation is used to partition them into subgroups. Finally, for subgroups in which the difference of orientation among buildings is small, similarity is used to partition them into super subgroups. Experimental results indicate that grouping results produced by this method have a better agreement with that of human performance.
Rights: All rights reserved
Access: open access

Files in This Item:
File Description SizeFormat 
b23071783.pdfFor All Users7.71 MBAdobe PDFView/Open

Copyright Undertaking

As a bona fide Library user, I declare that:

  1. I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
  2. I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
  3. I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.

By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.

Show full item record

Please use this identifier to cite or link to this item: