|Title:||Detection and generalization of changes in settlements for automated digital map updating|
|Subject:||Hong Kong Polytechnic University -- Dissertations.|
Topographic maps -- Data processing.
|Department:||Department of Land Surveying and Geo-Informatics|
|Pages:||165 leaves : ill. (some col.) ; 30 cm.|
|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.|
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